{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30ebf8f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| echo: false\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "43832dcf-0452-4503-aace-8f38e5e23723",
   "metadata": {},
   "source": [
    "# About NeuralForecast\n",
    "\n",
    "> **NeuralForecast** offers a large collection of neural forecasting models focused on their usability, and robustness. The models range from classic networks like `MLP`, `RNN`s to novel proven contributions like `NBEATS`, `NHITS`, `TFT` and other architectures."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9bdd2aa5",
   "metadata": {},
   "source": [
    "## 🎊 Features\n",
    "\n",
    "* **Exogenous Variables**: Static, historic and future exogenous support.\n",
    "* **Forecast Interpretability**: Plot trend, seasonality and exogenous `NBEATS`, `NHITS`, `TFT`, `ESRNN` prediction components.\n",
    "* **Probabilistic Forecasting**: Simple model adapters for quantile losses and parametric distributions.\n",
    "* **Train and Evaluation Losses** Scale-dependent, percentage and scale independent errors, and parametric likelihoods.\n",
    "* **Automatic Model Selection** Parallelized automatic hyperparameter tuning, that efficiently searches best validation configuration.\n",
    "* **Simple Interface** Unified SKLearn Interface for `StatsForecast` and `MLForecast` compatibility.\n",
    "* **Model Collection**: Out of the box implementation of `MLP`, `LSTM`, `RNN`, `TCN`, `DilatedRNN`, `NBEATS`, `NHITS`, `ESRNN`, `Informer`, `TFT`, `PatchTST`, `VanillaTransformer`, `StemGNN` and `HINT`. See the entire [collection here](../capabilities/01_overview)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cebf377",
   "metadata": {},
   "source": [
    "## Why?\n",
    "\n",
    "There is a shared belief in Neural forecasting methods' capacity to improve our pipeline's accuracy and efficiency.\n",
    "\n",
    "Unfortunately, available implementations and published research are yet to realize neural networks' potential. They are hard to use and continuously fail to improve over statistical methods while being computationally prohibitive. For this reason, we created `NeuralForecast`, a library favoring proven accurate and efficient models focusing on their usability."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50d28044",
   "metadata": {},
   "source": [
    "## 💻 Installation\n",
    "\n",
    "\n",
    "### PyPI\n",
    "\n",
    "You can install `NeuralForecast`'s *released version* from the Python package index [pip](https://pypi.org/project/neuralforecast/) with:\n",
    "\n",
    "```python\n",
    "pip install neuralforecast\n",
    "```\n",
    "\n",
    "(Installing inside a python virtualenvironment or a conda environment is recommended.)\n",
    "\n",
    "\n",
    "### Conda\n",
    "\n",
    "Also you can install `NeuralForecast`'s *released version* from [conda](https://anaconda.org/conda-forge/neuralforecast) with:\n",
    "\n",
    "```python\n",
    "conda install -c conda-forge neuralforecast\n",
    "```\n",
    "\n",
    "(Installing inside a python virtualenvironment or a conda environment is recommended.)\n",
    "\n",
    "### Dev Mode\n",
    "If you want to make some modifications to the code and see the effects in real time (without reinstalling), follow the steps below:\n",
    "\n",
    "```bash\n",
    "git clone https://github.com/Nixtla/neuralforecast.git\n",
    "cd neuralforecast\n",
    "pip install -e .\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3aeb537",
   "metadata": {},
   "source": [
    "## How to Use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea728145-d6cb-4eab-888a-6033b3388953",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "import pandas as pd\n",
    "from utilsforecast.plotting import plot_series\n",
    "\n",
    "from neuralforecast import NeuralForecast\n",
    "from neuralforecast.models import NBEATS, NHITS\n",
    "from neuralforecast.utils import AirPassengersDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c929c07b-0558-4799-ba1b-a926af018b43",
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.getLogger('pytorch_lightning').setLevel(logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73dfc69f-16ec-4d52-9d89-2a1131e29d92",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Seed set to 1\n",
      "Seed set to 1\n"
     ]
    },
    {
     "data": {
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OL1iwAJPJBMCKFSswmUx1Dzc3N3r06MF777132rV++5g8efIp+86ePRuz2cyLL75Ydyw2Nva099c+Zs2aBcDKlSu54IIL8Pf3x93dnS5dunDTTTdRUVHR5PeHkmciIiIiIiIiIiIi0qxOHE4mMcQfgAujgup1T0jHcGLTrdVqPyVnNltsImLl6urK888/T25u7lmvO3DgACdOnGDv3r3cfvvt3Hnnnfz8888nXTN58mROnDhx0uPzzz8/Za0PP/yQhx9+mA8//LDu2KZNm+ru+frrr0/a88SJE/zzn/9k7969TJ48mYEDB7Jq1Sp27drF66+/jrOzM9XV1U1+X5y7NlZEREREREREREREpAk27Usiz88bs2EwzNezXvc4OjnSx2JhD7AqO795AxRpBoZhUFVRaZe9HZ2d6irG6mvChAkkJCQwe/ZsXnjhhTNeFxwcjK+vLwD33nsvr732Glu3bmX8+PF117i4uBAaGnrW/VauXElpaSlPPfUUH3/8MWvXrmX48OEEBf03we7v73/KngBz5swhNDT0pDg7dep02uq2xlDyTERERERERERERESa1cqsPPDzprvJhIejud73jQnw5jNgvwFFVdV4NuBeEXurqqjkrd+/eO4Lm8Fd7z2Ek4tzg+4xm808++yzXHfdddx7771ERkae9XrDMFiyZAlJSUkMGTKkwTF+8MEHXHvttTg5OXHttdfywQcfMHz48HrdGxoayokTJ1i1ahWjR49u8N7nYve2jSkpKVx//fUEBATg5uZGr1692Lx5c915wzB4/PHHCQsLw83NjQkTJnDo0KGT1sjJyWHmzJl4e3vj6+vLLbfcQlFRUUs/FRERERERERERERH5jfKSMnY6Wus4xgb7Nujevl0i8S0sodpkYn2+5p6JNLfLL7+cvn378sQTT5zxmsjISDw9PXF2dmbq1Kk88cQTpySwvv/+ezw9PU96PPvss3XnCwoKmDdvHtdffz0A119/Pf/5z3/qndu56qqruPbaaxkzZgxhYWFcfvnlvPHGGxQUFDTiWZ/KrpVnubm5jBgxgnHjxrFo0SKCgoI4dOgQfn5+dde88MILvPbaa3z00Ud06NCBxx57jEmTJrF3715cXV0BmDlzJidOnGDZsmVUVlZy88038/vf/57PPvvMXk9NRERERERERERERIDj+45xLDQAgAkRgQ26N6xzJLFrd7PdK5pfUrOYEODdHCGKNAtHZyfueu8hu+3dWM8//zwXXHABDz744GnPr169Gi8vL8rLy9m4cSP33HMP/v7+3HnnnXXXjBs3jrfffvuk+2pbMAJ8/vnndOrUiT59+gDQt29fYmJi+PLLL7nlllvOGaPZbGbOnDk888wz/PLLL2zYsIFnn32W559/no0bNxIWFtaYp17Hrsmz559/nqioKObMmVN3rEOHDnX/bxgGr776Ko8++iiXXnopAB9//DEhISEsWLCAGTNmsG/fPhYvXsymTZsYOHAgAK+//jpTpkzhpZdeIjw8vGWflIiIiIiIiIiIiIjU+fVQCiUh/rhYLPT3dm/Qvc6uzvSqrGY7sCrbNhUlIi3FZDI1uHViazB69GgmTZrEI488wqxZs04536FDh7r5Yz169GDDhg38/e9/Pyl55uHhQefOnc+4xwcffMCePXtwdPxvmspisfDhhx/WK3lWKyIightuuIEbbriBp59+mq5du/LOO+/w5JNP1nuN07Fr28bvvvuOgQMHctVVVxEcHEy/fv3417/+VXf+6NGjpKWlMWHChLpjPj4+DBkyhHXr1gGwbt06fH196xJnYB1q5+DgwIYNG067b3l5OQUFBSc9RERERERERERERMT21uRZ27D1c3LE2aHhL0mPCrRWmyUYkF1RZdPYROT0nnvuORYuXFiXizkbs9lMaWlpvdfetWsXmzdvZsWKFWzfvr3usWLFCtatW8f+/fsbFbOfnx9hYWEUFze9xatdK8+OHDnC22+/zQMPPMD//d//sWnTJu69916cnZ256aabSEtLAyAkJOSk+0JCQurOpaWlERwcfNJ5R0dH/P396675rdmzZzc56ygiIiIiIiIiIiIiZ1ecV8Red+v4nQvC/M9x9en16BJJUGI6mX7erM0r4uIGzk0TkYbr1asXM2fO5LXXXjvlXEZGBmVlZXVtG//9739z5ZVXnnRNeXn5KTkaR0dHAgMD+eCDDxg8ePApc9IABg0axAcffMCLL7541vjeffddtm/fzuWXX06nTp0oKyvj448/Zs+ePbz++uuNeMYns2vlmcVioX///jz77LP069eP3//+99x222288847zbrvI488Qn5+ft3j+PHjzbqfiIiIiIiIiIiISHt0ZO9RkkKtSbMLQhuXPAvvGkXMiWwAlqfn2iw2ETm7p556CovFcsrxuLg4wsLC6Ny5M3/+85+5/fbbT0lYLV68mLCwsJMeI0eOpKKigk8++YTp06efds/p06fz8ccfU1lZedbYBg8eTFFREXfccQc9evRgzJgxrF+/ngULFjBmzJjGP+kadq08CwsLIz4+/qRj3bt35+uvvwYgNDQUgPT09JOGu6Wnp9O3b9+6azIyMk5ao6qqipycnLr7f8vFxQUXFxdbPQ0REREREREREREROY2VR05QGRGMl8VCvKdbo9Zw83SnV0Ulm4HVmnsm0izmzp17yrHY2FjKy8vr3h47diyGYdRrrdOtVysrK+uM5x5++GEefvjhc+7Zr18//v3vf58zlsaya+XZiBEjOHDgwEnHDh48SExMDGAdOhcaGsrPP/9cd76goIANGzYwbNgwAIYNG0ZeXh5btmypu+aXX37BYrEwZMiQFngWIiIiIiIiIiIiIvJbhmGwrsg6B2mwqzMOJlOj1xoe4I3JYnDcMEgtq7BViCIip2XX5Nn999/P+vXrefbZZ0lISOCzzz7jvffe4+677wbAZDJx33338cwzz/Ddd9+xa9cubrzxRsLDw7nssssAa6Xa5MmTue2229i4cSO//vor99xzDzNmzCA8PNyOz05ERERERERERESk/crPyOWgjycAEyKDmrRW1y5RhGbnAbAmr6ipoYmInJVdk2eDBg3im2++4fPPP6dnz548/fTTvPrqq8ycObPumocffpg//OEP/P73v2fQoEEUFRWxePFiXF1d66759NNP6datG+PHj2fKlCmMHDmS9957zx5PSURERERERERERESAg3sTSQn2A2BcsG+T1gqPiyK2Zu7Zysz8poYmInJWdp15BjBt2jSmTZt2xvMmk4mnnnqKp5566ozX+Pv789lnnzVHeCIiIiIiIiIiIiLSCL8cS8cSE0ZwtYUYV+cmreXl7018SRnrgNU5BRiGgakJbSBFRM7GrpVnIiIiIiIiIiIiInL+MSwGG8srARjm5WaTRNfQIF/M1dVkWAyOlJY3eT0RkTNR8kxEREREREREREREbCrreDqHA3wAmBgVbJM1O3SJJCIjD4A1uZp7JiLNR8kzEREREREREREREbGp3XsTSa9Jno0O9LbJmhFx0cScyAJgVXaBTdYUETkdJc9ERERERERERERExKZ+Ts0GINZiIcjZySZr+ob40a2wGIA1uYVYDMMm64qI/JaSZyIiIiIiIiIiIiJiM9VV1WyttgAw0s/LZuuaTCYGBfriXFlFvsVgb1GpzdYWEflfSp6JiIiIiIiIiIiIiM2kHU7hSIg/YLt5Z7Wi46KISssBYLXmnolIM1HyTERERERERERERERsZvP+Y+R5e+BgGAz387Tp2hFx0cTWzD1bk1to07VF2rNZs2ZhMpl47rnnTjq+YMECTCYTACtWrMBkMpGXl3fK/bGxsbz66qunvF17z9keK1asoLq6mueee45u3brh5uaGv78/Q4YM4f3332/Op31GjnbZVURERERERERERETOSysz8sDbi24m8HQ023TtgMggOucV8DOwLreISouBk4PJpnuItFeurq48//zz3H777fj5+dlkzeHDh3PixIm6t//4xz9SUFDAnDlz6o75+/vz5JNP8u677/LGG28wcOBACgoK2Lx5M7m5uTaJo6GUPBMRERERERERERERm6goq2CH2ZowGxvoa/P1HRwc6Bvkh1tZBSWuzmwvLGGQj4fN9xGxBcMwKLFY7LK3u4NDXcVYfU2YMIGEhARmz57NCy+8YJM4nJ2dCQ0NrXvbzc2N8vLyk44BfPfdd9x1111cddVVdcf69OljkxgaQ8kzEREREREREREREbGJlP3HOBoaAMCEyMBm2SMqLoqYtGz2x4axOrdQyTNptUosFjqt2mWXvQ+P7oWHuWGVn2azmWeffZbrrruOe++9l8jIyGaK7lShoaH88ssv3HXXXQQFBbXYvmeimWciIiIiIiIiIiJy3jEMg0W7TjDpH6sY9cIv5JVU2DukduHXQ8mUuLngbLEwsJmSWhFx0cRo7plIs7j88svp27cvTzzxxBmviYyMxNPT86RHUlJSk/Z95ZVXyMzMJDQ0lN69e3PHHXewaNGiJq3ZFKo8ExERERERERERkfOGYRisScjixSUH2JmcX3d8TUIW03qH2zGy9mF1TiEE+tHX0YyzQ/PUbgTHhtEpKw+ATfnFlFRbcDerTkRaH3cHBw6P7mW3vRvr+eef54ILLuDBBx887fnVq1fj5eV10rGxY8c2ej+A+Ph4du/ezZYtW/j1119ZtWoVF198MbNmzeL9999v0tqNoeSZiIiIiIiIiIiInBe2JuXy4uIDrDuSDYC7s5kIXzcOZRSx43iekmfNrKSgmN1uLgCMC/Nvtn3Mjmbig/3wKi6l0MONrQXFjPTzOveNIi3MZDI1uHViazB69GgmTZrEI488wqxZs04536FDB3x9fU865ujY9HSTg4MDgwYNYtCgQdx333188skn3HDDDfz1r3+lQ4cOTV6/IZQ8ExERERERERERkTbtQFohLy09wLK96QA4mx2YOTSau8d1ZsWBTB78agc7juefYxVpqsS9iRyvnXcWFtCse0XGRROVlsPeThGsz1PyTMTWnnvuOfr27UtcXJzdYoiPjweguLi4xfdW8kxERERERERERETapOM5Jfxj2UG+2Z6CYYCDCab3j+SPE7oQ6ecOQN8oHwB2peRTVW3BUe39ms3Ko6lUhAbhVW2hh6dbs+4V3jWK6AVrapJnRc26l0h71KtXL2bOnMlrr73WIvtdeeWVjBgxguHDhxMaGsrRo0d55JFH6Nq1K926dWuRGP6X/qUQERERERERERGRNiejoIxpr69h/jZr4uyinqEsvX80L17Vpy5xBtAx0BNPF0dKK6tJyFSSpTmtLSwDYKCrEw4mU7PuFdY5guiauWeb84upsFiadT+R9uipp57C0kJfW5MmTWLhwoVcfPHFdO3alZtuuolu3bqxdOlSm7SEbChVnomIiIiIiIiIiEib89O+DPJLK4kJcOf1a/vRO9L3tNc5OJjoFeHDuiPZ7DieR7dQ75YNtJ0ozC5gp7/1fTspMqjZ93NycaaHvzduZeWUurqws7CUgT4ezb6vyPlq7ty5pxyLjY2lvLy87u2xY8diGMZp709MTDzr22fbB+C2227jtttuq0+oLUKVZyIiIiIiIiIiItLmrD6UCcAV/SLPmDir1SfKen675p41m417j5Ie4IODYXBxeGCL7BkdH0NUei4A69S6UURsSMkzERERERERERERaVOqqi38mpAFwOiu507U1M4923E8rznDatcWplmTWPGVVQQ4t0zDs6j4DkSlZwNo7pmI2JSSZyIiIiIiIiIiItKm7EjOp6CsCm9Xx3NWncF/K88OpBdSWlHdvMG1Q4Zh8KuD9aXmi/y8Wmzf0M4RxGZZqwk35BVRfYZ2ciIiDaXkmYiIiIiIiIiIiLQptS0bR3YJxOxgOuf1od6uBHu5UG0x2HtCrRtt7UBKFsf9vcEwuKZ7VIvt6+jkSL8gX5wrKimyGOwtKm2xvUXk/KbkmYiIiIiIiIiIiLQpqw/VtGzsElSv600mU12Fmuae2d5Xh5IB6FhYQqSXR4vuHRMfS2SGtWXkhvziFt1b5EwMVUG2Sg35uCh5JiIiIiIiIiIiIm1Gfmkl22tml43scu55Z7U096z5/FRcBsAYx5Z/uTmqewzR6TkArMvV3DOxLycnJwBKSkrsHImcTu3HpfbjdDYtM7lRRERERERERERExAbWHc6i2mLQMciDSD/3et9XO/dsR3Je8wTWTmWUV3DQ3RWAK2JCWnz/4NgwOuYVsgJYm1OAYRiYTOdu5SnSHMxmM76+vmRkZADg7u6uz8dWwDAMSkpKyMjIwNfXF7PZfM57lDwTERERERERERGRNmNVA1s21uod4QvAsewScosr8PNwtnVo7dLXCakYJhNh2fn0G9mzxfd3MDswKMiHj6uqyXU0k1BSThcP1xaPQ6RWaGgoQF0CTVoPX1/fuo/PuSh5JiIiIiIiIiIiIm2CYRisOpgJwOiu9W/ZCODj7kTHQA+OZBWzIzmPsXHBzRFiu7MwLQccHBhcUoajk31ebu7QvQMRmbkcCwtkfX6RkmdiVyaTibCwMIKDg6msrLR3OFLDycmpXhVntZQ8ExERERERERERkTYhMbuE5NxSnMwmhnQIaPD9faJ8OZJVzM7kfCXPbCC3soodNS3ppvh72S2OqPhYIr9azrGwQNbmFHJDeMMSqyLNwWw2NyhZI61Ly09wFBEREREREREREWmE1YesVWcDYvzwcGl4XUDvSB8AdhzPs2VY7dbizDyqTSaCcwoY0j3GbnH4RwTSpagEgLXZ1rlnIiJNoeSZiIiIiIiIiIiItAmrDlrnnY1q4LyzWn2ifAHYkZynBIsNfJNknekUn5JJcGyY3eIwmUwMDfLDwWIh3WJwvKzCbrGIyPlByTMRERERERERERFp9SqrLaw7bE2ejenauORZfJg3jg4msooqSMkrtWV47U5hVTVrS8oBGOPogIPZvi81d+4WTWhWPgDr84vtGouItH1KnomIiIiIiIiIiEirt/VYLsUV1fh7OBMf5t2oNVydzHSvuXfH8Xxbhtfu/JRdQJXJhH9+EYM7hts7HKJ6dCA6PQewtm4UEWkKJc9ERERERERERESk1Vt9yFp1NrJzIA4Opkav0yfKOvdsZ3KeLcJqtxam5wLQLTGNaDvOO6vlE+RLXGkZAL9mKTEqIk2j5JmIiIiIiIiIiIi0eqsPZQIwqktgk9bpE+kLwPbjeU2MqP0qrq7mlxxrdVevjBwCo0LsHJHV8GBfMAyOWwwyyivtHY6ItGF2TZ797W9/w2QynfTo1q1b3fmysjLuvvtuAgIC8PT0ZPr06aSnp5+0RlJSElOnTsXd3Z3g4GAeeughqqqqWvqpiIiIiIiIiIiISDPJLa5gZ4q1mmh0I+ed1eoT5QvArpR8qi1GU0Nrl5ZnF1JmgE9hCYPCAjA1oRLQlrp1iyWkJqmnuWci0hR2rzzr0aMHJ06cqHusWbOm7tz999/PwoUL+eqrr1i5ciWpqalcccUVdeerq6uZOnUqFRUVrF27lo8++oi5c+fy+OOP2+OpiIiIiIiIiIjIeaqq2sIbvxxixHO/sGxv+rlvEJtak5CFYUBciBch3q5NWqtTkCcezmZKKqpJyCiyUYTtyw+ZeQDEHUsjqhW0bKwV2T2GqJq5Z2tqYhQRaQy7J88cHR0JDQ2tewQGWsuu8/Pz+eCDD3jllVe44IILGDBgAHPmzGHt2rWsX78egKVLl7J3714++eQT+vbty0UXXcTTTz/Nm2++SUVFhT2floiIiIiIiIiInCcOphdyxdtreWnpQVLySpm35bi9Q2p3bNWyEcDsYKJXpHXu2Q61bmywcouFZdnW6q64YyeIio+1b0D/w8PXk/hKa1cyzT0Tkaawe/Ls0KFDhIeH07FjR2bOnElSUhIAW7ZsobKykgkTJtRd261bN6Kjo1m3bh0A69ato1evXoSE/Len7qRJkygoKGDPnj1n3LO8vJyCgoKTHiIiIiIiIiIiIv+rqtrC2ysOM+21NexMzsfZbH0pbXeKXktqSYZhsPpQFgCjmtiysVZt68YdyXk2Wa89WZlTSFG1Ba/iUjpXVOIXFmDvkE4yPNgXgCPVFnIrNd5HRBrHrsmzIUOGMHfuXBYvXszbb7/N0aNHGTVqFIWFhaSlpeHs7Iyvr+9J94SEhJCWlgZAWlraSYmz2vO1585k9uzZ+Pj41D2ioqJs+8RERERERERERKRNS8goZPo763h+8X4qqi1c0C2YH/84CoCUvFJyitX1qKUcziziRH4Zzo4ODOngb5M1+0b6AkqeNcYPmdaKrq7H0oiJj8Vkah3zzmr16haNf34RhsnEJs09E5FGcrTn5hdddFHd//fu3ZshQ4YQExPDf/7zH9zc3Jpt30ceeYQHHnig7u2CggIl0EREREREREREhGqLwQdrjvDS0oNUVFnwcnXk8WnxXDkgEpPJRIdAD45mFbMrJZ8xNqqCkrNbedBadTakgz+uTmabrFlbebb/RCFlldU2W/d8V2kxWFLTDrHbsTQipw6zc0SnioyLIXrTAXJ8PFmVlsPEQB97hyQibZDd2zb+L19fX7p27UpCQgKhoaFUVFSQl5d30jXp6emEhoYCEBoaSnp6+inna8+diYuLC97e3ic9RERERERERESkfTuSWcRV76zl2R/3U1FlYUzXIJbeP5qrBkbVVdf0jLC+EL87RfOUWoot553VCvNxJdDThSqLwZ5UteGsr7V5ReRVVeNRWk5kRg5R3WPsHdIpXDxc6VltAWCN5p6JSCO1quRZUVERhw8fJiwsjAEDBuDk5MTPP/9cd/7AgQMkJSUxbJj1LxqGDRvGrl27yMjIqLtm2bJleHt7Ex8f3+Lxi4iIiIiIiIhI2/Tt9hQu+udqtibl4eniyPPTezH35kGE+ZzcHalXhPWPsHeq3V+LKK+qZv2RbABG27DSz2Qy0TfKmgjdcTzPZuue777PzAOgS1IaPgE+eAf52jWeMxlZM/fsoMWguKravsGISJtk1+TZgw8+yMqVK0lMTGTt2rVcfvnlmM1mrr32Wnx8fLjlllt44IEHWL58OVu2bOHmm29m2LBhDB06FICJEycSHx/PDTfcwI4dO1iyZAmPPvood999Ny4uLvZ8aiIiIiIiIiIi0kaUV1Xz+Ld7KK+yMKpLIEvuH801g6JPO8upV4QvALtTVK3UErYk5lJWaSHIy4W4EC+brt1Hc88apNowWFQz7yzuWBpR3WNa3byzWv26ReNTVIJFc89EpJHsmjxLTk7m2muvJS4ujquvvpqAgADWr19PUJD1r0j+8Y9/MG3aNKZPn87o0aMJDQ1l/vz5dfebzWa+//57zGYzw4YN4/rrr+fGG2/kqaeestdTEhERERERERGRNubnfRnkl1YS6u3K3JsHE+HrdsZre9RUnqXklZJTXNFSIbZbK/+nZaOtEzW1c892Jqu1X31syCsmq7IK98oqYk5kE9U91t4hnVF4lyiiM3IBWJGSZedoRKQtcrTn5l988cVZz7u6uvLmm2/y5ptvnvGamJgYfvzxR1uHJiIiIiIiIiIi7cTXW5IBuLx/BGaHsydovF2d6BDowdGsYnal5DPGhq0E5VSrD1oTH6O72P793DvS2rbxaFYxeSUV+Lo723yP80V+ZRXPHEkFoNOxNMyGQWQrnHdWy8nFiV6GwS7gV809E5FGaFUzz0RERERERERERFpSZmE5Kw5aq5um94+s1z09I6xJl90pelG+OWUWlrP3hLU95sgugTZf39fdmdgAd0DVZ2eTWVHJFdsT2FpQghcwZPdh/MIC8PT3tndoZzUyxBeAfYZBucVi32BEpM1R8kxERERERERERNqtb7enUG0x6BvlS+dgz3rd06umdeMuJVya1a8J1qqzHuHeBHq6NMseta0bdxzPa5b127rksgou25rAnqIygpwdeSwjm6C8IiK7td6qs1pD4mLwKC2jysGBbZp7JiINpOSZiIiIiIiIiIi0W/NqWjZOH1C/qjOAXhG+AOxS5VmzWnWwdt5Z87XG7BPpC8CO5Lxm26OtOlxSxqVbD3G4tJwIFye+7dcFh51HAIiKj7VvcPUQ2jGc6Ezr1+gvx9LtHI2ItDVKnomIiIiIiIiISLu0JzWf/WmFOJsduKR3eL3v61FTeZaSV0pOcUVzhdeuGYbBqkO1885s37KxVm3l2fbj+RiG0Wz7tDV7i0q5dGsCKeWVdHZ34bv+XQitqiI7OQOAiG7Rdo7w3MyOZnrXjDD8NbvAvsGISJuj5JmIiIiIiIiIiLRLtVVnF8aH4OPuVO/7vF2d6BDoAaj6rLnsTyskq6gcNyczA2L9mm2fHuHeODqYyCoq50R+WbPtY08nEpLZuPBX9q/dTeqhZIrzis6aKNycX8zl2xLIqqyip6crn3YIxSE5k+1LNwIQGBWMu7dHS4XfJKOCfQHYbRhUWZQcFZH6c7R3ACIiIiIiIiIiIi2tstrCd9tTAZg+IKLB9/eM8OFoVjG7U/IZ07X52gq2V8sPWCuchnb0x8XR3Gz7uDqZ6Rbmxe6UAnYczyPc163Z9rIHS7WFha9+RWlhyUnHHZ0d8Q70xSfYF58gP7yDfHFycWJNQTGzPdwpd3AgJqeASV8sY0HZydWVkd1jW/AZNM3IbtG47k+mzMWJnflF9PfzsndIItJGKHkmIiIiIiIiIiLtzooDmWQXVxDo6cLoRszU6hXhzcIdqexKVuWZrRmGUVcVOKlHaLPv1zvSl90pBWxPzuOiXmHNvl9LOpGQTGlhCU6uzoR0CCM/M4+i7AKqKqrISc0iJzWr7tqDUSEsGNuPagcHOqRkcsXyLThXVWNyMOHp54VXgA++of4MuGiIHZ9Rw4REhxL96x4Ohgcy/3Aq/QfG2TskEWkjlDwTEREREREREbGxnOIKUnJLySwqI6OgnMzCcjIKy8koLKv7/6yicsZ3C+HNmf3tHW67NG/LcQAu7xeOo7nhk016RvgAatvYHDYfy+VIZjHuzmam9an/LLrG6hvpy2cbkthxPK/Z92ppiTsOA9CxX1cm33EpANVV1RRm55OfmUdBZh55GXn8UFnNN+GBWEwmhlVV8WTHUAIGz8Q7wAcPX08cGvE10hqYHExMqK7mIPBZfgl/qqzCz0kviYvIuek7hYiIiIiIiIiIDS3Zk8Ydn2zhLCOF6vyw6wQPZhXXzc+SlpFbXMEv+61tAacPiGzUGrXJs5S8UnKKK/D3cLZZfO3dl5usic1pvcPwdGn+ly/7RPkCsDulgGqLgdnB1Ox7tpSjOxIAiO3Tqe6Y2dGMb4g/viH+nCiv4IUDySzLLgDgmlB/Xo6LwvE8eh9cExfFvOQsMvy9eTspg//r1PwJWRFp+5Q8ExERERERERGxEYvF4MUlBzAM8PdwJszHlWAvF4K9XAnyciHY24UgT+t/X1h8gA1Hc6zJtjGdzr242Mx3O1KprDboGeFNt1DvRq3h7epEh0APjmYVs0tzz2ymsKySH3aeAOCaQVEtsmfnYE/cnc0UlVdxIK2Q+PDGfU60NgXZ+WQnZ2AymYjtdfL3GMMw+OxEDn9LSKGw2oKzycSfYkP5Q0wwDqbzJ3EG0KlfV8Yt3cSXI/vwr+MZ3B4VTICzXhYXkbPTdwkRERERERERERtZsieNhIwivFwdWfHQWLxdnc547cV9wtlwNIfFu5U8a2m187Sm929c1VmtnhE+HM0qZreSZzazcMcJSiur6RTkQf9ovxbZ0+xgYkTnQJbtTee7HannTfIssabqLLRzBK6ebnXHj5WW8+CB46zOLQKgv7c7/+gWTZyHq13ibG6Ozo5cHBXEyqx80gJ9eDMpg8c7q/pMRM6ubTarFRERERERERFpZQzD4PVfrC9W3zw89qyJM4CJ8SGYTLD9eB4n8ktbIkQBDqYXsislHyeziUv7RjRprV4R1iTLrmTNPbOVLzdbWzbOGBSNqQUroGoTqd9sS6baUo+eq21AbfKsQ5/OAFgMg/eTMxm78QCrc4twczDxt07hLOzf5bxNnNXqMbI3o7YfBODDlEwyyivtHJGItHZKnomIiIiIiIiI2MDyAxnsPVGAh7OZm0d0OOf1wd6uDKiprFm6J725w5MaX9dUnY2LC27ynLLauWe7UpQ8s4X9aQXsOJ6Ho4OJy/s3LbHZUBd0C8bP3Yn0gnLWJGS16N7NoaqikuN7EwGI7dOZhJIyLtuWwKOHUii1WBjm68Evg7pxR3Qw5vOsTePphHaKYEBlFeGZuZRZDN5IymjUOoe3HqC6qtrG0YlIa6TkmYiIiIiIiIhIExmGwWs/W6s8rh8Wg189kzKTe4YCsHh3WrPFJv9VVW1h/rYUAKYPaFrLRvhv8iwlr5Sc4oomr9fefbnJWnV2YXwIgZ4uLbq3s6MDl/SxtvKrTbC2Zcn7jlFVUYWnvxc/ODgwftMBNuYX42F24PmukXzdtzMd3Fv2fWxPJpOJ+BG9GLXNWn32UWoWJ8ob9jWbsj+J7/85j0/++h5VlVXNEaaItCJKnomIiIiIiIiINNGvCdlsP56Hi6MDt47sWO/7JvWwJs82HM1W8qUFrE7IIrOwHH8PZ8bFBTd5PW9XJzoEegCqPmuq8qpqvqlJbF49KMouMVw5wLrvkj1pFJS17bZ+R2taNhYM6cFfDqVQbjEY5+/FysHduCkiEId2UG32W91G9KJDahaR6TmUWwxeO1b/6jPDMFj79QryPVyJ6B6Lo5NjM0YqIq2BkmciIiIiIiIiIk30+i+HALh2cDRBXvWv5ojyd6dHuDcWA37aq9aNza22ouiSPuE4O9rmZbHa6rPdSp41ydI96eSVVBLm48roLkF2iaFnhDddQzwpr7Lww84TdonBFgzDIHFHAoXuLrwbGgDADeEBfNa7I5GuTWtV2pZ5B/oQ1T2G0dsOAPBpajbJZfX7o4Wk3UfYmFvInItHsah/HIZxfszFE5EzU/JMRERERERERKQJNh7NYcPRHJzNDtw+pv5VZ7Um11SfLd6j1o3NKb+kkqU1CcorbdCysVavCG8AdiUredYU/9lsbdl41YBIzA72qYoymUxM72/93GjLrRtzT2STl13At2P7k2dAT083nu4cgakdVpv9VvcRvYhJy6FTdj4VhsE/j537jxYMw+DdFdv5fNJQSl2d2V1RSYnF0gLRiog9KXkmIiIiIiIiItIEbyy3tke7cmAkYT5uDb6/du7ZmkNZFLbxVnGt2fe7UqmostAt1Ise4d42W7e28kxtGxvveE4Jqw9lAXDVQPu0bKx1eb8IHEyw+VguR7OK7RpLYx3dnsDqvl05HuyPp9mB93rE4mrWy8AAnQd2w9HZiWEb9gDw+YlsjpWWn/WeF9bv5cMeHalyNHOBjwfz+3XGw2xuiXBFxI70XVNEREREREREpJG2H89j1cFMzA4m7hzTqVFrdA72pGOQBxXVFpYfyLRxhFKrtpJoev9Im1bg1CbPUvJKNbeukb6q+diM7BxIlL+7XWMJ9nZldFdr28j5W9tm9dmPiWms7dMZgJfioujoXv9Wsuc7ZzcXOg/sRlRGLr1Ky6ky4JXE01efWQyDpw6l8I+ySjCZuLCsnI/7KnEm0l4oeSYiIiIiIiIi0khv/GKtOrusb0SjX/Q3mUx1rRuX7FbrxuZwOLOIrUl5mB1MXNov3KZre7s60SHQA1D1WWNUWwzm1bRsvHqQfavOatW2bpy/NQWLpW3NtjqWV8icDtbP8Wv9PLksxM/OEbU+3Uf2AmDIrzsB+CothyMlJ1eflVss3LX3GG8lW/+g4YJdCbw3qheOdmopKiItT8kzEREREREREZFG2JtawE/70jGZ4K5xjas6q1XbunH5gQzKKqttEZ78j9oKojFdgwj2crX5+rXVZ7uVPGuw1YcySc0vw8fNiYnxIfYOB4AL40PwcnUkJa+U9Uey7R1OvVVZDH6/4wglrs6EFxQzu1fDZzC2B5HdY/D09yIoOYNhZgcswMuJ//3DhbzKKmbsOMyCjDwcLBamrdrOfTGhuHnatypSRFqWY30uuuKKKxq88DvvvENwcHCD7xMRERERERERaQveXGGtOpvaK4xOQZ5NWqtXhA/hPq6k5pex6mAmE2sq0aTpqi0G87emAP+tKLK1XhHeLNyRyq5kJc8a6j81VWeX94vA1al1tMNzdTJzcZ9wPtuQxLytyQzvHGjvkOrlhaMn2GExcK6o5C/l5ZpzdgYODg50G96Lzd+v5YI9R1jXLZb56bn8MSYEd7MD1+04wsGSMtyBS5ZtJL6olL4TB9k7bBFpYfX6DrpgwQKcnZ3x8fGp1+OHH36gqKiouWMXEREREREREbGLhIwiftx1AoB7Lujc5PVMJhOTaqrPFu9R60Zb2pGcx4n8MrxcHRnfvXn+0Lu28kxtGxsmu6icZXut86auaSUtG2vVJloX7UqjqLzKztGc2y/ZBbyWlAHARWt3Maynqs7OJn5kbwCMjXu50McDA/jzweNM3XKQgyVlhDg7csvKrXQ4kc3AqcNwdtPcOJH2pl6VZwCvvfZavSvJ5s2b1+iARERERERERERau7dWJGAYMDE+hG6h3jZZc3KPUOb8mshPe9OprLbgpKoRm1h5wDqzaHSXoGarbKpNnqXklZJbXIGfh3Oz7HO++WZbCpXVBn0ifegeZpuvI1vpH+1Lx0APjmQVs2jXCa4a2LqSe/8rtayCe/YdA6Df/mP0TcsmvGvrjbc18AsLILRTOGmHU7ksM5dlzs6syysGIM7DlUcLith79AQevp70Hj/AztGKiD3U66ew5cuX4+/vX+9FFy1aRERERKODEhERERERERFprZKyS/h2eypgm6qzWgNj/Qn0dKagrKpNzVlq7VYetCbPxnQNarY9vF2d6BDoAaj6rL4Mw+DLTdaWjVe3sqozsFaDTh9grT77umZmXmtUZTG4c+8xciqr6VhVzYRNe4nu2RGzY+togdmadRvRC4CyNTu4PNgXgOG+nnzdI5bE79YAMOiSETg6O9krRBGxo3olz8aMGYOjY72L1Bg5ciQuLiplFREREREREZHzz9srE6i2GIzpGkTvSF+brWt2MHFhfE3rxt1q3WgLucUV7EzOA2B0MybPQK0bG2prUh6HMopwdXLg4j7h9g7ntC7vF4HJBOuP5HA8p8Te4Zyk0mKQVFrOU4dT2ZBfjKfZgRlb9uNYbSG2j+2S+uezuCE9MDuayUrK4DFPV+b17cQXfTpybOV2SvKL8Qr0oeeYfvYOU0TspMH1/2PGjOHjjz+mtLS0OeIREREREREREWm1UvNKmbfFWoVy73jbv0A9uWbu2ZI96VRbDJuv396sScjCYkC3UC9CfVybda9eEda2g7uSlTyrj//UVJ1N7RWOt2vrrOwJ93VjRKdAAOZvTWnRvQ3DYPm2g7zx3a98lJLF3w+nctfeY1yy9RAD1u4hZuUOBq/fx3vJ1srKZ6OCMPYlAhDbu1OLxtpWuXq60aFvFwAS1u5mpJ8XRnklm39YC8CQS0epgk+kHWtw8qxfv348+OCDhIaGctttt7F+/frmiEtEREREREREpNV5b9URKqsNhnUMYEBM/Udc1NewjgF4uTqSVVTOtqRcm6/f3tS2bGzuqjNQ5VlDFJVXsXCntfXpNa2wZeP/mj7AOprm663JGEbLJbQLMvO4PruQZ7w8+PPBZF5PymB+ei4b84tJKa/EAjiZTMS4OvNEp3B6HM8AILhDGB6+ni0WZ1vXvaZ144F1u7FUW9i+dCNlRaX4hQXUnROR9qnBybNXX32V1NRU5syZQ0ZGBqNHjyY+Pp6XXnqJ9PT05ohRRERERERERMTuqi0GC3dYX/C/fUzHZtnD2dGBCd1DALVubCrDMFjVAvPOatUmz1LySsktrmj2/dqyzzckUVJRTcdADwbF+tk7nLOa1CMUD2czSTklbEpsuYS2T7AfHYHgnHw6H0/nCkcHHusUzrs9Yvihfxd2DO/BsTG92TAsnjujg0nckQBAB7VsbJCY3p1w83KnJL+Yg+v3sHXRBgCGXj4aB3ODXzoXkfNIo74DODo6csUVV/Dtt9+SnJzMddddx2OPPUZUVBSXXXYZv/zyi63jFBERERERERGxq+3H88gursDL1ZERnQObbZ9JPWrmnu1Ja9FKl/PN/rRCMgrLcXMyM7AFEjTerk7EBrgDqj47m+UHMnhu8X4Abh4Ri8lksnNEZ+fu7MjU3mEAfF3TsrWlrBrfn38Wl3LVz5uJn/sjl5aUcmmwHwN8PAhxccKh5n1XXVVN0u4jgFo2NpTZ0UzcsB4A/DTnRypKywmMCqbLoO52jkxE7K1J6fONGzfyxBNP8PLLLxMcHMwjjzxCYGAg06ZN48EHH7RVjCIiIiIiIiIidvfzPmvHnTFdg3BqxoqEMV2DcHVyIDm3lD2pBc22z/mutmXj8E4BuLTQ3KJekb6Akmdnsic1n3s+3Uq1xeCK/hFcPzTG3iHVy/T+kQD8sOsEpRXVLbavyWRizPUT6TQwjuqqahb+8yuykjNOuS714HEqyipw8/YgpEN4i8V3vug+sjcA1ZVVAAy7Ygwmh9ad1BWR5tfgn/QyMjJ4+eWX6dmzJ6NGjSIzM5PPP/+cxMREnnzySd5//32WLl3KO++80xzxioiIiIiIiLQbldUW7vp0C9f9az1fbT5OcXmVvUNq137eZ33RuratYnNxczYztmswAEv2qHVjY6080HLzzmr1ivAGYFeykme/lZpXyu/mbqK4oprhnQJ47orerb7qrNagWH+i/N0oKq9q8a9JBwcHJt9+KeFdo6goKefbl76gMPvkpPrRmpaNsb07KenTCEHRIQREWr9PhHQMp0O/LnaOSERagwYnzyIjI3n//fe56aabSE5OZt68eUyePPmkf+x69+7NoEGDbBqoiIiIiIiISHvz5abj/LgrjbWHs3lo3k4G//0n/jxvJ1uO5aidXws7nlPCgfRCzA4mxsY1fzJmcs+a1o2ae9YoReVVbD6WA7TMvLNatXPPVHl2ssKySn43dxPpBeV0DfHk7esH4OzYduZJOTiY6qrPvt7asq0bARydnbj4vqvwDw+kKLeQBS99Tllxad15zTtrGpPJxPArxxIUHcLYGya1maSuiDSvBv8r9fPPP7Nv3z4eeughgoJO/8OHt7c3y5cvb3JwIiIiIiIiIu1VSUUV//z5EACTeoQQG+BOcUU1X24+zvS31zHhlZW8u/IwGYVldo60faht2Tgwxg9fd+dm329ct2CczCYOZRSRkFHU7Pudb9Ydzqay2iAmwJ3YQI8W27c2eZaSV0pucUWL7duaWStot7I/rZAgLxc+nDUIHzcne4fVYLXJszUJWaTmlZ7jattz9XDj0gdn4OnnRU5qFgtf/YqqiiryM3LJPZGNg9mB6J4dWjyu80XHfl257ulbCe2otpciYtXg5NmoUaOaIw6ee+45TCYT9913X92xsrIy7r77bgICAvD09GT69Omkp6efdF9SUhJTp07F3d2d4OBgHnroIaqq1MZCRERERERE2rY5vyaSWVhOlL8br1/bn+UPjuU/tw/jygGRuDmZOZxZzOxF+xk2+xdu/Whz3XwnaR4/72+Zlo21fNycGN4pEFDrxsZYVfP10JJVZwDerk7EBrgD52f12bfbU7jvi22sO5xdr+sNw+DRb3az+lAWbk5mPrxpEJF+7s0cZfOI8ndncAd/DAPmbWla9ZlhGLy78jDHc0oadJ93gA+X/mkGzm4upB48zpJ3v+XIdusfWYR3icLF3bVJcYmIyH/VK3nWv39/cnNz673oyJEjSUlJqff1mzZt4t1336V3794nHb///vtZuHAhX331FStXriQ1NZUrrrii7nx1dTVTp06loqKCtWvX8tFHHzF37lwef/zxeu8tIiIiIiIi0trkFlfwzorDAPzpwjicHR0wmUwM7uDPS1f1YdOjE3h+ei8GxPhRbTH4aV86N324kRUHMuwc+fmpsKyS9UesyYLx3YNbbN/a1o2Ldp9osT3PB4ZhsOKg9WuhpZNnAL0ifYHzL3lWXlXNowt2s2B7Ktf+az1Xv7uOtQlZZ20h++byBL7cfBwHE7xxXT96Rfq0YMS2d93gaAD+vf4Y5VXVjV5nw9EcZi/az+RXV1HUwFmWgVHBXPzHqzA7mknYvJ+1X1m7f8WqZaOIiE3VK3m2fft2duzYwc6dO+v12L59O+Xl5fUKoKioiJkzZ/Kvf/0LPz+/uuP5+fl88MEHvPLKK1xwwQUMGDCAOXPmsHbtWtavXw/A0qVL2bt3L5988gl9+/bloosu4umnn+bNN9+kokKl8SIiIiIiItI2vbUigcLyKrqHeXNJn1NbSHm6OHLNoGi+vnM4Pz0whgvjrdVQc35NbOFI24dVB7OorDboGOhBxyDPFtv3wvgQzA4mdqcUcDhTrRvrKzG7hOM5pTiZTQztGNDi+w+Ktb6+9fXWZCyW82c24dqEbArLqnB3NuNsdmDj0Ryue38DV7+7jjWHTk2iLdiWwktLDwLw5CU9GN9CVZvNaWrvMEK9XcksLOe77amNXueNX6wzyi7vH4Gni2OD74/sHsPE318CQFWFNfkW26dTo+MREZFT1btt4/jx4+nbt2+9HqWl9e/7e/fddzN16lQmTJhw0vEtW7ZQWVl50vFu3boRHR3NunXrAFi3bh29evUiJOS///hOmjSJgoIC9uzZc8Y9y8vLKSgoOOkhIiIiIiIi0hqk5JXy0bpjADw8OQ4HB9NZr+8c7Mlfp3QHYNWhzAa3AZNzq5131pJVZwCBni6M6mJt3bhgW/07/LR3K2sqMAfF+uPRiMREU13eLwIvV0eOZBbz0770c9/QRtRWQF45IJKVD49l1vBYnB0d2JSYy/UfbODKd9ax8mAmhmGw/kg2D8/bCcDvR3fkhmGxdozcdpzMDswaEQvAB2uOnrXq7ky2JuWyJiELRwcTt49ufMKr65B4Rl93IQB+YQH4hwc2ei0RETlVvX6COHr0aIMXjoyMPOc1X3zxBVu3bmXTpk2nnEtLS8PZ2RlfX9+TjoeEhJCWllZ3zf8mzmrP1547k9mzZ/Pkk0+eMz4RERERERGRlvbqsoNUVFkY0sGfsfVsORcb6MGoLoGsPpTFF5uSeGhSt2aOsv2othgsr0nG2KNy5or+kaw4kMn8rSncP6HrOZOpQt38P3u0bATwcnXi+qExvL3iMO+sPMyF8SGYTG3741ZZbWHpXmsi8KKeYYT5uPG3S3pw59hOvLPyMJ9tSGLLsVxu+nAjfaN8OZJZREW1hSm9QvnL5PPr+9G1g6J57edD7E8rZPWhLEY38POsruqsXwRR/k2b/9Zv0mCCYkLwDvBp859jIiKtTb0qz2JiYhr8MJvNZ13z+PHj/PGPf+TTTz/F1bVlh1k+8sgj5Ofn1z2OHz/eovuLiIiIiIiInM6h9EK+3poMwJ8v6tagF0NrZ/F8uSmZympLs8TXHm1NyiW3pBIfNycGxvid+wYbmxgfgpeLIyl5pWxMzGnx/duasspq1h+xvp/GxNkneQZw84hYnM0ObE3KY1Nirt3isJUNR3LIK6kkwMOZwR38646HeLvyxMU9WP3wOG4Z2QFXJwe2H8+joKyK/tG+vHJ13/Mu4evj7sTVA6MA+NfqIw26d3dKPr/sz8DBBHeNs82MsshuMXgH+dpkLRER+a96t220tS1btpCRkUH//v1xdHTE0dGRlStX8tprr+Ho6EhISAgVFRXk5eWddF96ejqhodaBuaGhoaSnp59yvvbcmbi4uODt7X3SQ0RERERERMTeXlhyAIsBk3qE0D+6YYmaCfEhBHm5kFVUzrK950+rOHurbbs3Ni4IR3PLv4zi6mTmol7W1zi+2arWjeeyOTGX0spqQrxdiAvxslscwV6uTB8QAcC7Kw/bLQ5b+bGmZePEHqGYT5MMC/Z25bFp8ax++AJuH9ORi/uE868bB+LqdPY/rm+rbhnZAQcTrD6UxYG0wnrf9+Zya9XZxX3C6RDo0VzhiYiIDdgteTZ+/Hh27drF9u3b6x4DBw5k5syZdf/v5OTEzz//XHfPgQMHSEpKYtiwYQAMGzaMXbt2kZGRUXfNsmXL8Pb2Jj4+vsWfk4iIiIiIiEhjbTmWw7K96TiY4KFJcQ2+38nswDU11RCfbUiydXjt1s/7rK85TLBDy8ZaV/S3jsb4YdcJyiqr7RZHW7DyoPXjNbpLkN3b2N02qiMmE/y8P6NBCZbWptpisHSPdTzKRT3P/MfqAEFeLjxyUXdev7YfAZ4uLRGeXUT5uzO55n3xfj2rzw6mF7Jot/X9eLeNqs5ERKT52C155uXlRc+ePU96eHh4EBAQQM+ePfHx8eGWW27hgQceYPny5WzZsoWbb76ZYcOGMXToUAAmTpxIfHw8N9xwAzt27GDJkiU8+uij3H333bi4nL//QIuIiIiIiMj5xTAMnl90AICrBkTRObhxFTMzBkdhMsGahCwSs4ptGWK7dCy7mISMIhwdTHZtATg41p8IXzeKyqvq5k7J6dXNO7Pjx6tWxyBPJvewJljeXdV2q882J+aQVVSBj5sTwzoF2DucVuPWUR0B+HZ7KhmFZee8/q2aqrPJPULpaseqSBERqR+7Jc/q4x//+AfTpk1j+vTpjB49mtDQUObPn1933mw28/3332M2mxk2bBjXX389N954I0899ZQdoxYRERERERFpmOUHMtiYmIOLowP3Xdil0etE+rkzpqs1afD5RlWfNdVPNVVngzv44+3qZLc4HBxMXN7P2gLwm5qZeHKq1LxSDqYX4WCCkZ0D7R0OAHeM6QTAd9tTSc0rtXM0jVNbLXVhfAhOdmhd2lr1j/ZjQIwfFdUWPl577KzXHs0q5rsdqQDcc4GqzkRE2oIG/4vXsWNHsrOzTzmel5dHx44dmxTMihUrePXVV+vednV15c033yQnJ4fi4mLmz59/yiyzmJgYfvzxR0pKSsjMzOSll17C0dGxSXGIiIiIiIiItJRqi8ELi61VZ7OGxxLm49ak9WYOiQHgqy3JlFepxV9T/FRT5TXeji0ba13e35o8W3Uoq15VLu3R6kPWqrO+Ub74ujvbORqrPlG+DO3oT5XF4IM1R+0dToNZLAaLd9evZWN7dNuoDgB8suEYJRVVZ7zu7RUJWAwYFxdEzwiflgpPRESaoMHJs8TERKqrT/3hu7y8nJQUDa4VERERERERaYhvt6ewP60Qb1dH7hzbqcnrjYsLItTblZziCpbsUYu/xsovrWRTYg4AE7oH2zka6BTkSd8oX6otBt9tT7V3OK1SbcvG0V3t37Lxf9VWn32+MYm8kgo7R9Mw247nkVZQhqeLIyO7tI5qvtbkwvhQov3dySup5Ostp68KTc4tYf5W62um91zQ+MpiERFpWfVOnn333Xd89913ACxZsqTu7e+++45vvvmGp59+mtjY2OaKU0REREREROS8U15VzSvLDgJwx9hONqmWcTQ7cM2gKAA+XX/2VmJyZisPZlJlMegc7ElMgIe9wwHgiprqs2+26Y+Xf6uq2sLqQ1kAda1LW4sxXYPoFupFSUU1n7Sxr8nFu08AML57MC6OZjtH0/qYHUz8bkQsAB+sOYrFYpxyzbsrj1BlMRjROYABMX4tHKGIiDRWvZNnl112GZdddhkmk4mbbrqp7u3LLruMGTNmsGzZMl5++eXmjFVERERERETkvPLZhiSSc0sJ8Xbh5uEdbLbujMFROJhgw9EcEjKKbLZue/LzvtqWjfavOqs1rXc4TmYTe1IL2J9WYO9wWpXtx/MoLKvC192J3pG+9g7nJCaTqa76bM6viZRVto12qoZh8OOu2paNYXaOpvW6amAU3q6OJGaX8NO+k6t90wvK+HLzcQDuGaeqMxGRtqTeyTOLxYLFYiE6OpqMjIy6ty0WC+Xl5Rw4cIBp06Y1Z6wiIiIiIiIi542i8ire+CUBgD+O74qbs+2qOsJ83Ligm3VO1+cbk2y2bntRVW1hxQFrC8AJrWDeWS1/D2fGxVmTed9sVfXZ/6pt2TiqSxBmB5OdoznVtN5hRPi6kV1cwbwztPdrbXanFJCSV4qbk7nVVfO1Jh4ujswcap01+f7qk+favbfqCBVVFgbG+DG0o789whMRkUZq8Myzo0ePEhho7XFcVqYBtSIiIiIiIiKN8Z9Nx8kurqBjoAdXD4y0+fozh0QDMG9LcpupdGktNh/LJb+0Ej93J/pHt642a7WtGxdsT6H6NC3i2qtVNcmz1prkcTQ7cNsoa3Xpv1YfaRMfu0U1LRsv6BZs0+T++WjW8FiczCY2Juaw43geANlF5Xy6wdqm854LOmMytb6kroiInFmDk2cWi4Wnn36aiIgIPD09OXLkCACPPfYYH3zwgc0DFBERERERETkf1c6tmjUiFkdzg389P6fRXYOI8HUjv7SSH3edsPn657Palo3jugW3uiqmcd2C8XFzIr2gnLWHs+wdTquQXVTOzpR8AEZ3CbRzNGd29aAo/NydOJZdUpeYaq0Mw2DRbmvLxsk9Q+0cTesX4u3KxX3CAWtyFKwz0MoqLfSO9Gm1SV0RETmzBv90/swzzzB37lxeeOEFnJ3/O8i4Z8+evP/++zYNTkREREREROR8lJBRyK6UfBwdTEzt1TyzhMwOJq4dHAVYZ6tJ/f20LwNoXS0ba7k4mrm4j/VzZr5aNwKwJiELw4DuYd4Ee7vaO5wzcnd25MZhsQC8u/IIhtF6q88OpBdyNKsYZ0cHxnVrPXP/WrNbR3YEYNHuNPamFvDxupqqs3GqOhMRaYsanDz7+OOPee+995g5cyZm839Ltvv06cP+/fttGpyIiIiIiIjI+ai26mxM1yACPF2abZ+rB0bh6GBi87FcDqQVNts+55PDmUUczSrGyWxiVCutYrq8n7XN5+LdaRSXV9k5GvtbeaB1t2z8XzcNj8XVyYFdKfmsPZxt73DO6Mdd1qqzMV2D8HRxtHM0bUN8uDcjOgdQbTG48cONFJVX0S3Uq1Um4UVE5NwanDxLSUmhc+fOpxy3WCxUVlbaJCgRERERERGxrdoX86a9vpr9aQX2Dqdds1gMFmxLBeDymvlVzSXY25UL460v3H5WM3tHzq62ZePQjgF4uTrZOZrT6x/tS2yAO6WV1Syuaa3XFtliFl9FlYVVh6ztK9tC8szfw5kZg6zzCN9ZedjO0ZzZ4pq2khepZWOD3DrKWn2WVVQOwN3jOuPQylq/iohI/TQ4eRYfH8/q1atPOT5v3jz69etnk6BERERERETEtn7el86qg5nsTingirfWtukX3Nu6zcdySckrxdPFsUUqEq4bYn2hfv62FEoqVKV0LrUtG8e34lZ1JpOJK/pbq89qqxjbmsW70+jxxBJunrORjIKyRq1xNKuYK99ZS1ZROV6ujgyI8bNxlM3jlpEdMDuYWH0oi901s9qam2EYHM8pqVeryISMIg6mF+FkNjFeVVMNMrZrEF2CPQHoGOjBlGZqyysiIs2vwcmzxx9/nHvuuYfnn38ei8XC/Pnzue222/j73//O448/3hwxioiIiIiISBN9tC4RAB83J0oqqrnjky28+tNBLJbWO3PnfFWb7LioZyiuTuZzXN10IzoFEu3vTmFZFd/vONHs+7VleSUVbDmWC9DqkwaX97NWLf56OIsT+aV2jqZhqi0GLyzeT7XFYPmBTCa+uoofd9X/c9MwDOZtSWbqa6vZmZyPj5sTr83oh7Njg1/msosof3em9bYmVd5ddaTZ97NYDP701Q5GvbCce7/YTmW15azX11adjegciI9b66y+bK1MJhP/N7U7Eb5uPH5xPGZVnYmItFkN/qni0ksvZeHChfz00094eHjw+OOPs2/fPhYuXMiFF17YHDGKiIiIiIhIEyRkFPJrQjYOJvj27hHcPCIWgFd/OsRdn27VzKQWVFZZzQ87W6ZlYy0HB1Nd9dmnG5NaZM+Wtikxh+X7M86ZFDiXFQcyqbYYxIV4EeXvbqPomkeUvzuDY/0xDOragLYVS/akcSSrGG9XR3qEe5NXUsldn27lgS+3U1B29pEg+aWV3PvFdh78agclFdUM6eDPoj+OYlwrrhQ8ndtHdwLgx10nSG9k5V19zV60j/lbrUn7hTtSufOTLWdtmbmopjJ5Sk9VTTXGuLhgfv3LBYyNa1ufkyIicrJG/UnOqFGjWLZsGRkZGZSUlLBmzRomTpxo69hERERERETEBj5aa511Nb57CLGBHjxxcQ9euLI3zmYHFu9JY/rba0nKLrFzlO3DigMZFJRVEebjytAOAS2275UDInEym9hxPK/F2sS1lGPZxcx4bz03z93EiOd+4cUl+zme07DP52PZxby1IoGXlx0AYHz3tvGi9xU1Cdj5W5Pr1Y6vNTAMgzeXJwAwa3gs39w1gnvGdcbBZG0tOvkfq1h7OOu0925OzGHKP1ezcEcqZgcTD02K47PbhhLu69aST8Em4sO9GRzrT7XF4IuNx5ttn/dWHeZfq48CcPOIWFwcHfhpXwa3fbz5tG1ck7JL2JNagNnBVDcvUUREpD1qG/XsIiIiIiIi0igFZZV8vTUZsL5QXevqgVF8/vuhBHq6sD+tkEveXMPahNO/YC22U9uy8ZK+4Ti0YDuvQE8XJtdUkXxc08LzfDFvSzLVNe1HMwrLeXP5YUa9sJwbPtjADztPUFF1+mq04zklvLPyMBe/voYxL67ghcUHOJ5TiquTA5f2bZmqwKa6qFcYzo4OHMooYk9qgb3DqZdVh7LYk1qAm5OZWSM64OzowIOT4vjqjmHEBLiTml/Gdf/awNPf762rjqqqtvDqTwe5+t11pOSVEu3vzrw7hnH3uM5tui3ezKHWitDPNyZR1cSqydP5eksyz/64H4D/m9KNJy7uwZybB+HubGb1oSxu+nAjhb+p9FtU07JxaEd//DycbR6TiIhIW9Hg5Jmfnx/+/v6nPAICAoiIiGDMmDHMmTOnOWIVERERERGRBpq3OZmSimq6BHsyvNPJlU4DYvxY+IcR9I70Ia+kkhs+3MhHaxPbTAVLW5NXUsHy/ZnAf+dVtaSbhsUA8O32VHKLK1p8/+ZQbTH4eos1OfzK1X14a2Z/RnUJBGD1oSzu/mwrw2b/zOwf93Eks4jk3BLeW3WYS99Yw6gXlvPcov3sSsnHwQQjOwcy+4perP3LeOJCvez5tOrNx82prjqoNkne2r1VU3V27eBo/P8nOTMgxp8f7x1V12L0gzVHueSNNfyyP50Z763n1Z8OYTGsXzs/3DuSftF+donflib3DCXAw5m0gjJ+2pdh07V/2Z/Ow1/vBOC2UR34fU2byOGdAvn3LUPwcnVkU2IuM9/fQF7Jf78f/FjTsvEitWwUEZF2zrGhNzz++OP8/e9/56KLLmLw4MEAbNy4kcWLF3P33Xdz9OhR7rzzTqqqqrjttttsHrCIiIiIiIjUj8Vi1FUZ3Tg8FpPp1AqNMB83/nP7MB6Zv4tvtqXwxHd72JtawDOX98TJrGYltvTDrhNUVFvoHuZNt1DvFt9/QIwfPcK92ZNawBebjnPn2E4tHoOtrT2cRWp+Gd6ujkzpFYark5kpvcJIyi7hy81JfLU5mYzCct5ddYR3Vx056V4HEwztGMDU3mFM6hFKoKeLnZ5F00zvH8EPO0+wcEcq/zele6v+ut1yLIcNR3NwMpu4bXSHU857uDjy7OW9mNA9mIfn7eJgehG/m7sZAE8XR56+rAeX94ts6bCbjYujmWsGRfHWisN8sv4Yk3uG2mTdrUm53PXpVqotBpf3i+CRi7qfdH5AjB+f3zaUGz/cyM7kfGa8t55/3zKEimoLO47nYTLBxB5q2SgiIu1bg5Nna9as4ZlnnuGOO+446fi7777L0qVL+frrr+nduzevvfaakmciIiIiIiJ2tPJQJonZJXi5OnLFWSqdXJ3MvHJ1H7qHefHcov18ufk4kX5u/GF8lxaM9vy3oKZl4+X9wu2yv8lk4qbhsTw8byefrD/GbaM64NiKEy318dVma7XVJX3DcXUy1x2PDnDnoUnduH9CV37Zn8HnG5NYeTATAxjSwZ+pvcOZ3COUIK+2mTD7X6O6BBHo6UxWUQVL96QztXfrrRh6a/lhwFo9FuZz5jllF3QLYcl9vvzfN7tYsiedvlG+vDajH9EB7i0Vaou5dnA0b688zJqELI5kFtExyLNJ6yVkFPK7uZsoq7QwpmsQL1zZ+7QtYntG+PDl74cy8/0N7E8r5Jp31zGhpopxUKw/wV6uTYpDRESkrWvwT8lLlixhwoQJpxwfP348S5YsAWDKlCkcOXLklGtERERERESk5Xy0NhGAqwZE4eFy9r+dNJlM/H50J56f3huAt1YcJi2/rLlDbDeO55SwKTEXkwku6WO/eVqX9AnHz92JlLxSm7eJa2n5pZUs2WNtMXfVgKjTXuNodmBij1Dm3DyYTX+dwJZHL+SL3w/jhqEx50XiDMDJ7MB1Q6wtOd9akdBq267uO1HAz/szMJngjjHnrnoM8HThnesHsPrhcXx95/DzMnEGEOXvzgVxwQB8uiGpSWudyC/lxg82kldSSZ8oX96+vv9ZKxG7hHjxn9uHEeHrxpGsYt6rqc68yEYVcCIiIm1Zg5Nn/v7+LFy48JTjCxcuxN/fH4Di4mK8vNpGf3AREREREZHz0dGsYlYcyMRkghtrZl3Vx5UDIhkY40dpZTXPL97fjBG2L7VVZyM6BRLqY7+KDlcnMzMGW2dK1SZX26qFO1Ipr7IQF+JF70ifc14f4Oly0oyt88nNw2NxdzazJ7WAFQcym3Wv/JJKNhzJJqOgYcn1t1dYq86m9Ayrd3WVyWQiyt8d82kqp84n1w+1fo+etyWZ0orqRq2RV1LBjR9sJDW/jI5BHsyZNQh353M3nIoN9ODL24cS+z/JSVu1jxQREWnLGty28bHHHuPOO+9k+fLldTPPNm3axI8//sg777wDwLJlyxgzZoxtIxUREREREZF6q511NrZrELGBHvW+z2Qy8cTFPbjkzTV8sy2FG4bF0D/ar5mibB8Mw+Cb7dbk2WVnaZ/ZUq4fGsO7Kw+z7kg2B9IKiQttm3/8+tUWa8vGqwZGnnaeX3vi5+HM9UNjeG/VEd5YnsDYuKAmv08MwyCtoIw9KQXsSS1gT2o+e1ILSMkrBSDQ04UFdw8n0u/cFWHHsov5fmcqwHkxa8/WRncNIsrfjeM5pSzcmcrVA09fSXkmpRXV3PrRZg5lFBHi7cLHvxvcoERxpJ87/7l9GH/6agddQ7zO2lJTRESkvWhw8uy2224jPj6eN954g/nz5wMQFxfHypUrGT58OAB/+tOfbBuliIiIiIiI1FtxeRXzamZB3TQ8tsH394r04cr+kXy1JZknF+7lmzuHn3ZmjtTPrpR8jmQW4+rkwKQeIfYOhwhfNybGh7J4TxofrUvk2ct72TukBjuYXsiO43k4OphaRUKyNbh1ZAfmrk1ky7Fc1h/JYVingAavUVhWyb9WH2VbUi57UwvILq447XVuTmayisr53dxNzLtzON6uTmdd991VR7AYMKZrED0jzl0l2N6YHUxcNziG5xfv59P1xxqUPLNYDP74xTY2H8vF29WRj383pF4Jzd8K9nbl37cMafB9IiIi56sGtW2srKzkd7/7HeHh4Xz++eds3bqVrVu38vnnn9clzkRERERERMS+5m9NprC8ig6BHozuEtSoNR6aHIeHs5kdx/P4pqbloDRO7fvvwvhQvM6RZGgptUnVb7amkF9Sad9gGuGrzccBGNctmEDP82N2WVMFe7tyTU3S5c3lCQ2+3zAM7v9yO6/9fIjVh7LILq7A7GAiLsSLK/pF8OjU7nx+21B2PDGRXx4cQ7CXCwfTi7j7061UVlvOuG5GQVldMv8uVZ2d0dUDI3E2O7AjOZ+dyXn1vu+dVYdZujcdZ0cH3r9pUJutJBUREWltGpQ8c3Jy4uuvv26uWERERERERKSJDMPgo3XHAOuss8ZWjAV7ufKH8V0AeH7xforLq2wWY3tSVW1h4Q5ru7orWlGF1NCO/sSFeFFaWc1XW47bO5wGqay21CUkrxoQaedoWpfbx3TE0cHEmoQsth/Pa9C987em8NO+DJzNDjx5SQ++vXsEe56cxJL7R/PKNX25dVRHhnUKwMfNiTAfNz64aRBuTmZWH8riie/2YBjGadd9f81RKqotDIjxY3AHfxs8y/NTgKcLU3pZZ419sv5Yve5ZeziLl5YcAODJS3ro/SsiImJDDUqeAVx22WUsWLCgGUIRERERERGRpvo1IZuEjCI8nM1c2cTEws0jYokJcCejsJy3VjS8kkVgdUIWWUUVBHg4M7JLoL3DqWMymeqqzz5ed4xqy+kTH63RigOZZBVVEOjpzLhuwfYOp1WJ9HOva2P5xi/1/5pNyy/jbwv3AHDfhV24aXgsfaJ8cXUyn/GeXpE+vHZtP0wm+GxDEv9afeSUa/JLKvm0JhF097hO7X423blcPzQGgO92pJ6zIjQtv4x7P9+GxYArB0QyY1DD5qSJiIjI2TU4edalSxeeeuoprrzySmbPns1rr7120kNERERERETsZ+7aRACmD4hscotAF0czf53SHYB/rT5KUnZJU8NrdxbUVEhd3CccJ3ODfwVvVpf1C8fb1ZGknBJWHMiwdzj1Vtuy8bK+Ea3ufdoa3Dm2EyYT/LQvnf1pBee83jAM/vz1TgrLqugT5cvvR3Ws914Xxofw6NR4AGYv2s/i3Wknnf9oXSLFFdV0C/ViXJwSnecyIMaPbqFelFVamLc1+YzXVVZbuPuzrWQVVdA9zJunL+2pxKSIiIiNNfinzA8++ABfX1+2bNnCe++9xz/+8Y+6x6uvvtoMIYqIiIiIiEh9HM8p4ef96QDcOCzWJmteGB/CiM4BVFRZePbHfTZZs70oKq9iyR5rMuGyVtSysZa7syPX1FSr1CZdW7usonJ+2W9N9F01UJU2p9MpyJMpvcIAeHP54XNe/5/Nx1l5MBNnRwdevqo3jg1MSP5uRCw3DI3BMOC+L7exo6ZdZElFFXN+PQrAXeM6K7lTDyaTqa767NP1x87YCnP2j/vZciwXL1dH3p7ZHzfnM1cIioiISOM0OHl29OjRMz6OHDm1RF9ERERERERaxr/XH8MwYFSXQDoHe9pkTZPJxOPTeuBggsV70lh7OMsm67YHS3anUVZpoWOgB30ifewdzmndMDQWkwlWH8ricGaRvcM5pwXbUqiyGPSO9CEu1Mve4bRad4/tDMAPO1M5mlV8xutS8kp5+ntrUvzBiV3pHNzw96nJZOKJi+MZGxdEWaWFWz/eTHJuCZ9vPE5uSSUxAe5M6RnauCfSDl3WLwJPF0eOZBWz9nD2Kee/35nKhzVJyZev6kNsoEdLhygiItIuqL+BiIiIiIjIeaC0opovN1nb2d1ko6qzWnGhXnXVEE8t3EtVtcWm65+vFmy3tmy8rF9Eq626iQ5wZ3zN3LCPW3n1mWEYzNtibWV3VRPn+Z3v4sO9Gd8tGIsBb59hXqFhGPx53k6KyqsYEOPHLSPr367xtxzNDrx+bT+6hXqRWVjOLXM3837NDLTbR3dqcDVbe+bp4sjlNZWq/1537KRzCRlF/HneTgDuGNOJiT2UlBQREWkujfrpJTk5mbfeeou//OUvPPDAAyc9REREREREpOUt2J5CfmklUf5ujOtm+9lC90/oio+bE/vTCvmiJkl3vjpTq7T6KqusZvWhTH5NsFbpXda39bVs/F83DY8FYN6WZArLKu0bzFnsTilgf1ohzo4OXNKndb9PW4O7L7BWn83fmkJKXukp5z/dkMSahCxcnRx48cremB2aluD1cnXiw1mDCPZy4UB6ISfyywj2cmH6AH2sGqr2jxWW7UsnLb8MgOLyKu78ZAvFFdUM7ejPgxO72jNEERGR855jQ2/4+eefueSSS+jYsSP79++nZ8+eJCYmYhgG/fv3b44YRURERERE5CwMw+CjmqqhG4fGNvlF8NPx83Dm/gld+NvCvby89AAX9w7Hx93J5vvY24tL9vPuyiN0DPKgZ7gPPSN86BXpQ3yYNx4up/8VuqCski3Hctl0NIeNR3PYmZxPRU113oAYP6ID3FvyKTTYyM6BdAry4HBmMV9vSWbWiA4ttndltYWS8up6fS59tcWatJ3UI/S8/Nyztf7RfgzvFMDaw9m8t/IwT17as+7c8ZySuhmGD0/qRscg27R5Dfd144ObBnH1u+sorazmtlEdcXHUPK6Gigv1YnCsPxsTc/h8YxL3TejCI/N3cSijiGAvF16/tr+q+URERJpZg5NnjzzyCA8++CBPPvkkXl5efP311wQHBzNz5kwmT57cHDGKiIiIiIjIWWw4msP+tELcnMxcPTCq2faZOTSGTzckcSijiH/+fIjHL45vtr3s4Vh2Me+uPEKVxeBgehEH04uYv83aetFkgk5BnvQM96ZnhA9BXi5sS8pjU2IO+04UYPlNsVqQlwuDY/25p6b6pzUzmUzcNDyWx7/dw8frjnHjsFgcmiEB+78yCsv4dH0Sn25IIq+kggcnxfH7UR3PuG9ZZTULaj4WatlYf/eM68zaw9l8sek491zQhSAvFywWg4fm7aCkoprBsf7Mqqk8tJVekT58cutgfk3IrqtqlIabOTSajYk5fLEpCW83J77bkYqjg4m3ZvYnyMvF3uGJiIic9xqcPNu3bx+ff/659WZHR0pLS/H09OSpp57i0ksv5c4777R5kCIiIiIiInJm768+CsDl/SOatSLHyezAY9PiufHDjXy8LpHrhkTTOdg2FSutwas/HaLKYjCqSyCzhseyKyWf3Sn57E4pIK2gjISMIhIyiliwPfWUe2MC3BkU68/gDv4MjvUnJsC91c45O50r+kfywuIDHMkqZnVCFmO6BjXLPjuT85jzayLf70ylsvq/GcfnFu1n/ZFsXr6qDwGepyYGlu1Np6CsijAfV0Z0DmyW2M5HwzoF0C/al21Jeby/5giPXNSdj9clsv5IDm5OZl68qnezJEoHxPgzIMbf5uu2J5N7hhLo6Ux6QTlPf78XgL9c1I2BsXq/ioiItIQGJ888PDyoqKgAICwsjMOHD9OjRw8AsrKybBudiIiIiIiInNWRzCJ+3p8OwC0jm7/d3uiuQUzoHsxP+zJ4fvF+/nXjwGbfsyUcSCtkwXZrZdOfJ3ejZ4QP47uH1J3PLCyvSaTlsysln4zCcnpH+tQlzEK8Xe0Vuk14ujhy5YBI5q5N5KO1iTZNnlVWW1i8O405vx5la1Je3fH+0b7MGtGBwrJKnlq4lxUHMpny2mpem9GPIR0DTlrjqy3JAEzvH9ksbUnPVyaTiXvGdeaWjzbzybpjXNQzjOcW7wfg/6Z0IybAw84Rypm4OForid9acRiAKb1CW+R7vIiIiFjVO3n21FNP8ac//YmhQ4eyZs0aunfvzpQpU/jTn/7Erl27mD9/PkOHDm3OWEVEREREROQ3PlhzFMOACd2D6WSjuUXn8peLurP8QCbL9qaz8WgOgzu0/UqIV5YdwDDgop6h9IzwOeV8kJcL47oFM65bsB2iaxk3Doth7tpElh/I4Fh2cZMTKznFFXy+MYl/rztGWkEZAE5mE9N6hzNreCx9onzrrh0Q48fdn27lcGYx1/5rPfdP6Mpd4zpjdjBxIr+U1YcyAbhSLRsb7IJuwXQP82bfiQJmvLeOskoLwzsFMHNIjL1Dk3OYOTSGf687RrivG89P792mqllFRETaunpPF33yyScpLi7mlVdeYciQIXXHxo8fz5dffklsbCwffPBBgzZ/++236d27N97e3nh7ezNs2DAWLVpUd76srIy7776bgIAAPD09mT59Ounp6SetkZSUxNSpU3F3dyc4OJiHHnqIqqqqBsUhIiIiIiLSFuUUVzCvpiLn1lEdW2zfzsGeXDPIOlvt7z/uwzCMc9zRuu04nseSPek4mOCBC7vaOxy76RjkyZiuQRiGNSnbFOkFZUz8x0peXHKAtIIyAj1d+OP4Lvz6lwv4xzV9T0qcAXQL9WbhH0YyvX8kFgNeXnaQGz/cQEZhGfO3pmAYMDjWn9hAVUo1lMlk4u5xnQAoq7Tg4Wzm+enN065RbCvC141fH7mAb+8ZgZdr87XkFRERkVPVu/Ks9pehjh3/+wuZh4cH77zzTqM3j4yM5LnnnqNLly4YhsFHH33EpZdeyrZt2+jRowf3338/P/zwA1999RU+Pj7cc889XHHFFfz6668AVFdXM3XqVEJDQ1m7di0nTpzgxhtvxMnJiWeffbbRcYmIiIiIyNmVVVaz5Vgu/aP9cHM22zucduuT9ccor7LQK8KHIS1c/XXfhC4s2JbCjuN5/Lgrjam9w1p0f1t6aekBAC7rF0GXEC87R2Nft4/uyMqDmXy2IYkbh8XQObhx74+//7CPrKIKYgPcuXd8F6b2DsPF8ezfK9ydHXn56j4M6xTAYwt282tCNlP+uQbHmiTPlQNVddZYF/UMo3PwIRIyivjr1Hii/N3tHZLUk7eSZiIiInZhMur5J4IODg6kp6cTFNQ8Q4Nr+fv78+KLL3LllVcSFBTEZ599xpVXXgnA/v376d69O+vWrWPo0KEsWrSIadOmkZqaSkiItRf9O++8w5///GcyMzNxdnau154FBQX4+PiQn5+Pt7d3sz03EREREZHzxd2fbuWHXSfwcXNixuAobhwWS4Svm73DalfKKqsZ+fwvZBVV8M8Zfbm0b0SLx/CPZQf558+HiAlwZ9n9Y3B2rHdzk1Zj/ZFsZry3HieziV/+NFZJBeDWjzbz0750RncN4qObBzW4VVzt+9RkgoX3jDxtG8xzScgo4p7PtrI/rRAAd2czm/46AQ+XBo9ulxqpeaUczixiZOdAtf8TEZF2Ra//S2M06Debrl274u/vf9ZHY1VXV/PFF19QXFzMsGHD2LJlC5WVlUyYMKHumm7duhEdHc26desAWLduHb169apLnAFMmjSJgoIC9uzZc8a9ysvLKSgoOOkhIiIiIiL1szkxhx92nQAgv7SSd1ceYdTzv3DnJ1vYeDSnzbfwayu+3Z5CVlEFYT6uTOlln6qv34/uSKCnC8eyS/hswzG7xNAUhmHw0hJr1dk1g6KUOKvx16ndcTKbWHUwk+UHMhp0b2W1hSe+tf4+PnNIdKMSZ2BtDbrg7hFcNyQagBuGxihx1kThvm6M6hKkxJmIiIhIPTToJ88nn3wSH5/G/eB7Jrt27WLYsGGUlZXh6enJN998Q3x8PNu3b8fZ2RlfX9+Trg8JCSEtLQ2AtLS0kxJntedrz53J7NmzefLJJ236PERERERE2gPDMHj2x30AXD0wkgvjQ5nz61HWHs5m0e40Fu1OIz7Mm1kjYrmkTziuTmrp2BwMw+D91daZVDePiMXJbJ+KLw8XR+6/sAt//WY3r/2SwBUDIttUi7EVBzLZfCwXF0cH/nBBF3uH02p0CPTgdyM68O6qIzzz/T5Gdg6qd1Xhx+uOcSC9ED93Jx6cGNekOFydzDx7eS/+PKkb3m5KnImIiIhIy2nQT58zZswgODjYpgHExcWxfft28vPzmTdvHjfddBMrV6606R6/9cgjj/DAAw/UvV1QUEBUVFSz7ikiIiIicj5YvDuNrUl5uDmZ+dPEOEK8XbkwPoQDaYXMXXuU+VtT2HuigIfn7eS5Rfu5bnA0t47qgK97/VqqS/2sPJjJoYwiPF0cmTE42q6xXDMwig/WHOVIZjHvrjzMQ5O62TWe+rJYjLpZZzcNjyXE29XOEbUu91zQma+3JnMkq5iP1yVy66iO57wno7CMV5cdBODhyd1s9nXv4952ErIiIiIicn6o958nNldZv7OzM507d2bAgAHMnj2bPn368M9//pPQ0FAqKirIy8s76fr09HRCQ0MBCA0NJT09/ZTztefOxMXFBW9v75MeIiIiIiJydpXVFp5fvB+A20Z1OCnZEBfqxewrerP+kfH8eXI3wn1cySmu4I3lCdw0ZxPVFrVytKXaqrNrBkXZvdLL0ezAXyZbE2bvrz7KifxSu8ZTX4v3pLEntQBPF0fuGNPJ3uG0Ol6uTjw0yVo59s+fD5FVVH7Oe55btJ/C8ip6R/pw9UD9gaqIiIiItF31Tp611NwCi8VCeXk5AwYMwMnJiZ9//rnu3IEDB0hKSmLYsGEADBs2jF27dpGR8d8e7MuWLcPb25v4+PgWiVdEREREpL34bEMSidklBHo68/szJBv8PJy5c2wnVj08jrdm9sfLxZEdx/P4z+bjLRzt+WtvagFrErJwMFlbNrYGF8aHMCjWj/IqC/+oqTxqzaotBi/XVJ3dMrID/h6qjDydKwdE0TPCm8KyKl5eevaP6+bEHOZvTQHgqUt7YnbQXC0RERERabvqnTyzWCw2b9n4yCOPsGrVKhITE9m1axePPPIIK1asYObMmfj4+HDLLbfwwAMPsHz5crZs2cLNN9/MsGHDGDp0KAATJ04kPj6eG264gR07drBkyRIeffRR7r77blxcXGwaq4iIiIhIe1ZYVsk/fz4EwB8ndMXT5ewd4B3NDkzpFcZ9F3YF4IXF+8krqWj2ONuD99ccAeCiXmFE+rnbORork8nEI1O6AzBvSzL70wrsHNHZfbMthcOZxfi6O3HrqA72DqfVMjuYeHxaDwC+2JTEntT8015XbTF4/Ns9gLWNZ98o35YKUURERESkWdhnqnSNjIwMbrzxRuLi4hg/fjybNm1iyZIlXHjhhQD84x//YNq0aUyfPp3Ro0cTGhrK/Pnz6+43m818//33mM1mhg0bxvXXX8+NN97IU089Za+nJCIiIiJyXnpn5WFyiivoGOTBjEH1b8d247AYuoZ4kltSySttoCKptUsvKGPhjlQAbqvHDKqW1D/ajym9QrEY8Pyi/S2+//GcEjYl5lBRZTnrdRVVFl79yfq5eOeYTnjZue1laze4gz/TeodhGPDUwr2n7Urz2YZj7D1RgLerIw9PjrNDlCIiIiIitmUyWqofYytWUFCAj48P+fn5mn8mIiIiIvIbJ/JLGfviCsqrLLx7wwAm9TjzfOHTWXs4i+v+tQEHEyz8w0h6hPs0U6TnvxcW7+etFYcZFOvHV3cMt3c4pziaVcyFr6ykymLw2a1DGN45sNn2Kq+qZnNiLsv3Z7DiYCYJGUUA+Lo7Ma13GJf3i6B/tN8p87v/vS6Rx77dQ7CXCysfGoebs7nZYjxfpOSVcsFL1u8Bb83sz5ReYXXnsovKGffSCgrKqnjq0h7cOCzWfoGKiIiInIZe/5fGsGvlmYiIiIiItH6vLD1IeZWFQbF+TIwPafD9wzsFMq13GBYDnvh2T4vNUz7flFRU8emGJABubWVVZ7U6BHowc0g0AM8u2ofFYtuPdWpeKZ9tSOK2jzfT/6llzHx/A++vOUpCRhFmBxO+7k7klVTyyfokpr+9jjEvruCVpQc4nGlNrJVWVPPaLwkA/OGCzkqc1VOErxu318w5fPbHfZRVVtede2HxAQrKqogP82bmkBh7hSgiIiIiYlNnH1QgIiIiIiLt2r4TBczbmgzAI1O6n1LFU19/ndqdn/dlsPlYLgu2p3B5v0hbhtkufLU5mfzSSmIC3JnQveFJzJbyh/Fd+HprCrtTCli4M5VL+0Y0ec1PNxzj47XHOJBeeNLxQE8XxsYFMTYuiFGdg/B0dWTt4Sy+2ZbC4t1pJOWU8NovCbz2SwK9I30I93Ejs7CcSD83rhkU3eS42pM7xnTkq83HSc4t5f3VR7jngi5sP57Hl5uPA/DUpT0wOzTu+4OIiIiISGujyjMRERERETmj5xbtxzBgaq8w+kf7NXqdMB83/jC+MwDP/rifwrJKW4XYLlRbDD5YcxSAW0Z2aNVJikBPF+4YY62Me2HxAcqrqs9xx9ltSszhr9/s5kB6ISYT9I/25U8XdmXhPSPZ+H/jeemqPkzrHY6PuxNmBxOjugTxytV92fzoBP45oy/j4oIwO5jYmZzP4j1pANw3oSvOjvp1uCHcnR35y0XdAHhz+WFS80p5/NvdAFzRP4KBsf72DE9ERERExKZUeSYiIiIiIqe15lAWKw9m4mQ28dCkuCavd8vIDny1OZmjWcW89vMh/jo13gZRtg/L9lqrqHzcnLhyQOuv2rtlZEf+vf4YKXml/HvdsSa1mfzHsoMATOsdxtOX9sTPw7le97k7O3Jp3wgu7RtBVlE5P+w8wfc7Uwn2duXyfk2vhmuPLukTzsfrjrHlWC7XvLeO4zmleLn8N6kmIiIiInK+0J/aiYiIiEirlldSwcfrErn0jTVc/c46isqr7B1Su2CxGDz74z4AZg6JITbQo8lrujiaeeJia8Jszq+JHPpNCz45PYvF4F+rrVVn1w+Nxt259f8NpJuzmT9daE24vv5LAvkljas0XHc4m7WHs3Eym/jLRd3qnTj7rUBPF24aHstXdwznzev6t+rKvdbMZDLx+DTr1/DxnFIA7ruwK8FervYMS0RERETE5lr/b10iIiIi0u5YLAa/Hs7iP5uTWbInjYoqS925t5Yn8PBkVTk0twXbU9h7ogAvF0fuHd/FZuuOjQvmwvgQlu1N528L9/DJLUMaPUetrSmtqCajsIyMwnIyCspJL7D+f3ZROcUVVRSXV1NcXkVxRTUlFVXW/y+vprTS2vbQyWzipmGx9n0SDTB9QCTvrznCwfQi3lqRwCNTujfofsMw+MdP1qqzawZFEenn3hxhSgP1ifLlygGRzNuSTNcQT24cFmPvkEREREREbE7JMxERERFpNY7nlPDVlmS+3pJMSl5p3fFuoV4M7uDPx+uO8f7qo8wYFE10gF5Iby5lldW8tOQAAHeO64R/I6t9zuTxafGsPJjJrwnZLNqdxpReYTZdv7XYcTyPl5cdJCW3hIzCcgrLmlY1+buRHQj2bjsVPmYHE49c1J2b525iztpEbhgW06AE2K8J2Ww8moOzowN3j+vcjJFKQz1+cTxRfu5c2jccJ7Ma2oiIiIjI+UfJMxERERGxK8Mw+HFXGp9tPMavCdl1x71drfOKrhkURY9wbwCOZBazJiGLZ3/cxzs3DLBXyOe9uWsTSc0vI8zHld+N6GDz9aP83blzTCf++fMhnvl+L2PjgtpEK8KGKCqv4vf/3kx6QflJx12dHAj2ciXE24VgL1eCvFwI8nLBy9URd2dHPJzNeLg44uFixt3ZEU8XR9xrjrk6me30bBpvbFwQwzoGsO5INq8sPcgr1/St132GYfDKMmsC97rB0YT5uDVjlNJQ3q5O/HGC7SpSRURERERam/PrN1QRERERaVMMw+C5Rft5d9WRumMjOwdy1cBIJvUIPSVZ8Ni0eKa8tprFe9JYm5DF8M6BLR3yeS8lr5Q3fkkA4E8T45otYXPn2E58vTWZ5NxS3lp+mAcnxTXLPvbyytKDpBeUExPgzrOX97Imy7xd8XJxbDdtKsE6I+uRKd245I1f+WZ7Cr8b2YGeET7nvG/lwUy2JuXh4ujAXWM7tUCkIiIiIiIi/6X+CiIiIiJiF4Zh8Nzi/ybObh/dkdUPj+OTW4dwad+I0yZt4kK9uH5INABPfb+XqmrLKddI4xmGwZ/n7aSovIr+0b5c3i+i2fZydTLz2LR4AN5bdYTErOJm26ul7UnNZ+7aowA8dWlPRnQOpHOwF96uTu0qcVard6Qvl/QJxzDg+cX7z3m9YRj8Y5l11tkNQ2PaVKtKERERERE5Pyh5JiIiIiItzjAMXlhygHdXWhNnT13ag0emdCfK/9zzkO6b0BUfNyf2pxXyxabjzR1qu/LZxiTWJGTh4ujAS1f1wezQvImeifEhjO4aREW1hce+3Y1hGM26X0uwWAweXbAbiwFTe4UxpmuQvUNqFR6aFIeT2cTqQ1msPJh51mt/2Z/BjuR83JzM3KGqMxERERERsQMlz0RERESkRRmGwUtLD/D2isMA/O3ieG4cFlvv+/08nHngwq4AvLz0APkllc0RZrtzPKeEv/+wD4CHJ3ejY5Bns+9pMpn428XxuDg68P/t3Xd4VNXaxuHfpIc0CGmEYkIPvVdBUIoKKIIIFgSkWEAP6qceG5xjw3YUBQVEJBZERUFEQQFF6WDoJdQEQoA00vuU/f0RmCNHakhmEnju6+KSzOzZ+92Q1zDrmbXWmoNpfLHxaLlfs7x99ecxtiVk4uPx35l1UrLP3Zk+n7I0Fqvt3EFpyV5nJbPORnSJIMjX01ElioiIiIiI2Ck8ExERERGHOTMw/sGqkuBs8oAmjOwaednnubdjHRqG+pKRb2bqrwfKusxrjs1m8PS3O8kvttIhIpBRXSIcdu26wb48e0tjAF5dGsvh1FyHXbuspeUW2ZclfKJPI8ICtNzgX03oWR8/Lzf2JeWwaNvxcx7zy55k9pzIxsfDlXHd6zq4QhERERERkRIKz0RERETEYaauPMi03w4B8GL/JowqRXAG4ObqYp/V8/mGoxxKySmzGq9Fn288yoa4U3i7u/LWkBa4lPNyjf/r/s4RdGsQRKHZxhNfb8dcSfeym7J0H1kFZprU8GdE5+ucXU6FU83Hgwk96wMls0YLzdaznrfZDKauLAnDR3WNJNDHw+E1ioiIiIiIgMIzEREREXGQqSsP8N6vBwF4oV8Uo68vXXB2RrcGwfSKCsViM3jpx9irYr8sZziSlsfry0pmSz17a2Ouq+7j8BpcXEy8dWdLArzd2ZGYxfTTAWtlsinuFN9tTcRkglfuaIabq95qncuILhHUrOrNyaxC5q47ctZzy3YnsS8pBz9PN8Z206wzERERERFxHr2jExEREZFy997Kg0xdWRKcPX9rFGPKaGD8hX5RuLuaWH0glVX7U8rknNcSm83gqW93UGC20rlude7r6LzZUmEBXrwysBkA01cdYltChtNquVzFFhsvfL8bgGHt69CmTjUnV1Rxebm78mSfkj0LP1x1iPS8YgCsNoN3T886G90tkoAq7k6rUUREREREROGZiIiIiJSrab8etA+KP3tLY8aW4T5GEUE+PHB66cdXfoyl2FI5l/tzlrnrj/DnkQx8PFx5807HL9f4vwa0DOf2VuFYbQZPfLOD/GKLU+u5VHPWxnMwJZfqPh48c3MjZ5dT4Q1sVZOoGv7kFFnsswx/3HmCQym5BHi788AVzkoVERERERG5UgrPRERE5Jqz90Q2M34/TGZ+sbNLuaoVmq1MXryb/6woCc7+eUtjHryhXplfZ8KN9Qny9SAuLY/PNhwp8/NfrQ6n5vLmzyXLNT7XL4ragVWcXFGJl25rRo0AL+LT8nhtaaxTakjNKWLarwf540DqRZcDTczI5/3Ty5E+d2sUVaton66LcXEx8dytjQH4fOMR4lJzee/0zNRx3evi76VZZyIiIiIi4lwKz0REROSakZZbxLMLd9Fv2hre+HkfT32709klXbV2JmbS7/01fLrhKABP39yIh8ohOAPw83Ln6b4lA/HvrTxIWm5RuVznamK1Gfzfgh0UWWx0axDEPR3qOLsku4Aq7rw9pCUAX2xMcOhynDabwZebErjpP7/znxUHGPHJZm6bvo6fd5/EZjt3iPavH/ZSYLbSMTKQQW1qOqzWyq5bg2C6NQjCbDUYPmczcWl5VKvizoguEc4uTUREREREROGZiIiIXP2KLTZmr46j51u/M39zAoYBJhOs2JvMyr3Jzi7vqmKx2nj/14MM+nA9h1PzCPHzJHpUex7pUb9cr3tn21o0q1myDNx/lh8o12tdDT5eE8e2hEz8PN14Y3ALTCbnLtf4v7rWD7Ivx/n0tzvt+2KVp/1JOQyZtYHnFu0iu9BCvWAfvN1d2XU8i4e+2Eqfqav5bksiZut/lwZdsTeZlbHJuLmYeGVgswr351jR/fOWxphMcDyzAIAHb6iHr6ebk6sSEREREREBk3GxdUiuAdnZ2QQEBJCVlYW/v7+zyxEREZEyYhgGv+1L4ZWfYolPywOgWU1/JvVvym/7Upj5x2FqVvVm5RM34O3h6uRqHeeVH/eyfG8yVTxc8fF0o4qHK76ebvh4uuF7+msfTzdqBHhxfYMgQvy8Lum88Wl5PP71drYfywSgX/MavDKwGdV8HLOM3Z9H0hkycwMmE3w6qgPdGwY75LqOcOZ7uZqPB61rV72ikOZgcg79pq2l2GLjzcEtuKt97TKstOwUmq0MmLaWgym53Nw0jBn3tSmXcKqg2Mr7vx1k9uo4LDYDHw9XnuzTiPs7X0d2oYW56+KJXn+EnMKS/ddqVfPmwRvq0b95DfpPW8vxzAIe7lGPZ25uXOa1XQue+GY7C7ceJ8jXg9VP96SKh8IzERERESlbGv+X0lB4hppHRETkanQwOYeXftzLmoNpAAT5evB038YMblsLVxcT+cUWer+zmuOZBTzSox5PXyMD31uOpjN4xobLek2zmv70aBhCz8bBtKpdDVeXswMMwzD4YlMCr/0US4HZip+XGy/f3ozbW4U7fCbOswt3MX9zAgHe7vwwoSvXVfdx6PXLy7JdJ3l43lYAGob6Mqx9HQa1qXnJ+2sZhkHsyRx+2nWC77YcJym7kJ6NgvlkZPsKPVtq9/Es7vhwHWarwdtDWnJn21plev5V+1OYtHg3x9JLZj71bRrKv25rSo0A77OOyyk088XGBOasjSMtt2QWnIebC8UW2zUZwJel1JwiXvlpLwNb1aRn4xBnlyMiIiIiVyGN/0tpKDxDzSMiInI1ycwvZurKg3y+8ShWm4GHqwujro9gQs/6+Hm5n3Xs8j1JjPt8C+6uJpb9oxv1Q/ycVLVjGIbB3bM3sjEunf4tajC0fW3yiizkFlnJL7aQW2Qhr8hCXpGVvCIL+5Nz2JmYddY5Arzd6d4wmB4Ng7mhUTA2m8HT3+3k9/2pAHSpV523h7QkvKr3uUood0UWK8M+2si2hEwah/nx3cNd8LkKloG748N1bEvIPOsxDzcXbm0Wxt0d6tAhMvBvIZhhGOxLymHprpP8tPMkcadnXwKE+Xvx/fiuhAVc2qxCZ/pg1SHe+mU/vp5uLPtHN2oHVrnic6ZkF/LvH/fy086TAIQHePHv25vRu0noBV9XaLbyTcwxZv0RZ19q8OP729HrIq8TERERERHn0fi/lIbCM9Q8IiIiVwOL1ca8TQm8u/IAmflmAPo0CeX5flHnnX1kGAZjPo3h130pdKobyPyxnSr0LJwrtfZgGvfN2YSHqwurnupBzUsIuFJzilh9IJVV+1NYczCNrAKz/TmTCbzcXCkwW/Fwc+GZmxszqksELi7O/TNMzi6k/7S1pOYUcWvzMD64p3yW+3OULUczGDxjPR6uLvw8sRvrDqXx5eZjxJ7Mth9TN9iHYe1rM7hNLdJyi/lp5wl+3HWSuNT/BmYebi70bBRMvxbh3NQ4pNKEilabwV2zNrDlaAYdIgL5cmxH3FxLv3Xz8j1JPPnNDnKKLLiY4IGukTzeu+Fl/XmYrTaW7jqJyWTitpbhpa5FRERERETKn8b/pTQUnqHmERERqezWHEzl5R/3ciA5F4BGoX5MGtCErvWDLvraY+n59H73DwrNNt4d2pI7WpftsnAVhWEYDPxgHTsSsxjVNYLJA5pe9jksVhvbj2Wyan8Kv+9PZc+JkvCmabg/7w5tRcPQijNzb8vRdIZ9tBGz1eDpmxvxSI/6zi6p1MbP28pPu04ypG0t3hrSEij5+9yZmMVXfyawePsJ8outALiYwPaXf917uLpwQ6Ng+reowU1RofhWksDsfyWcyueW91aTV2y9omVWdx/PYvCM9RRZbLSsFcCrdzSnWc2AMq5WREREREQqEo3/S2koPEPNIyIiUlnFp+Xx6k+xrIxNBqBaFXee6NOIu9vXvqyZKWeWhQvy9eDXJ3sQ4O1+8RdVMmeWqPR2d2X10z0J9vO84nOmZBdyLKOA5jUD8HAr/Uyg8vLlpgSeW7QLkwnmjmxPj0aVbz+lY+n53PDWKmwG/DyxG43D/v5v1dwiC0t2nGD+5gR2Jmbh4epC94ZB9GtRg15RoX9brrSyWrLjBI/O3wbAR8Pb0qdp2GW9/lRuEbdNX8fxzAJ6Ngrm4xHt/7Z/n4iIiIiIXH00/i+lUTk/eioiIiLXtOxCM9N/O8TcdfGYrQZuLiaGd76OiTc1JKDK5QcFY7vVZeHWRA6n5vH2L/t5eWCzcqjaeaw2g/8sPwDAA9dHlElwBhDi70WIf8XdM+uejnXYdTyL+ZsTeGz+Nn6YcD0RQedewrOiil5/BJsB3RoEnTM4A/D1dOPuDnW4u0MdEjPy8fd2x/8qCcz+akDLcLYmZDB33RGe/GYHSx71u+S/T4vVxoQvt3E8s4DIIB+mDmut4ExERERERETOq+J9RFhERETkPKw2g682J3Dj27/z0eo4zFaDHo2C+XlidyYPaFqq4AxK9oI6E5h9sekoOxMzy7Bq5/tx5wn2J+fg5+XGuG71nF2OQ/3rtia0qVOV7EIL4z6PIbfI4uySLllOoZmv/zwGwAPXR17Sa2pVq3JVBmdnPHdrFO2uq0ZOkYWHvthCwenlKi/mtaX72BB3Ch8PVz4a3vaqnF0qIiIiIiIiZUfhmYiIiFQK2YVmBs1Yzz8X7iItt5i6wT7MHdWe6FEdqB/ie8Xn71IviIGtwjEMeH7Rbqy2q2Nla7PVxjsrSmadPXRDvVIHjJWVp5srM+9rS4ifJweSc/m/b3ZQWVYt//rPY+QWWagf4ssNDYKdXU6F4O7qwvR72hDk68G+pByeX7Tron+fC7cm8sm6eAD+c1crGlSgvflERERERESkYlJ4JiIiIpXCtF8PsuNYJn5ebrzYvwm/TOxOzzLew+r5fk3w83Jj1/Es5m06WqbndpZvtyRy9FQ+Qb4ejOwS4exynCLE34uZw9vi4erCz3uS+GDVIWeXdFEWq425644AMPr6SFy0xKBdWIAX0+5ug6uLiYXbjjNvU8J5j92VmMWzC3cB8NiN9bm52eXtkyYiIiIiIiLXJoVnIiIiUuHFpebag4Rpd7dm9PWRuLuW/T9jgv08ebpvIwDe+mU/KTmFZX4NRyo0W3n/14MAPNyjPj6e1+52t23qVOOl25sC8J8VB/htX7KTK7qw5XuTOZ5ZQKCPB3e0runsciqczvWq23v1pSV72X4s82/HpOUW8eDnMRRZbNzUOISJvRo6uEoRERERERGprBSeiYiISIX36k+xWGwGPRsF06OMZ5v9r3s6XkeLWgHkFFp47afYcr1WeZu3KYGTWYXUCPDi3o51nF2O0w3rUIf7OtXBMOAfX20nPi3P2SWd18dr4gC4r2MdvNxdnVxNxTSue11ubhpGsdXGI19sIT2v2P6c2WrjkXlbOZFVSN1gH94d1kqz90REREREROSSKTwTERGRCu2PA6n8ui8FNxcTL/RvUu7Xc3Ux8crAZphM8P32E/y8O6ncr1ke8oosfHh6ecLHbmqgAOa0Sf2b0j6iGjmFFp7+dge2Cri33daEDLYmZOLh6sJ9na9zdjkVlslk4s0hLYgM8uFEViGPzd9m36vw1Z9i2Ryfjq+nGx8Nb4e/17W115+IiIiIiIhcGaeGZ1OmTKF9+/b4+fkREhLCwIED2b9//1nHFBYWMn78eKpXr46vry+DBw8mOfnsZXYSEhLo168fVapUISQkhKeeegqLxeLIWxEREZFyYLbaePnHvQCM7BJBvWBfh1y3Ra2q3N+pJLR4ZN4WPt9wxCHXLUtz18VzKq+YiOpVuLNtLWeXU2F4uLnw7tBW+Hi48ueRDD7fWPH2tpuzNh6A21qFE+Ln5eRqKjZ/L3dm3tcWb3dX1h5KY+rKAyyIOUb0+iMAvDu0FfVDHPP/DREREREREbl6ODU8++OPPxg/fjwbN25kxYoVmM1m+vTpQ17ef5fQefzxx1myZAkLFizgjz/+4MSJEwwaNMj+vNVqpV+/fhQXF7N+/Xo+/fRToqOjmTRpkjNuSURERMrQFxuPcigll0AfDx69qYFDr/18vyYMaVsLmwEvLt7Dqz/trZCzlM4lK9/MrNUly/493rthuewPV5nVqlaFZ25pDMAbP+8jMSO/TM9vGAbJ2YVsjDvFV5sTeH3ZPuZvTrik75/EjHyW7ToJwOjrI8u0rqtVozA/Xh/cHIBpvx3i+UW7AZjYqwG9m4Q6szQRERERERGppEyGYVSYUaDU1FRCQkL4448/6N69O1lZWQQHB/Pll19y5513ArBv3z6ioqLYsGEDnTp1YtmyZfTv358TJ04QGlry5njmzJk888wzpKam4uHhcdHrZmdnExAQQFZWFv7+/uV6jyIiInJp0vOK6fHWKrILLbx2R3PuccKeXYZh8MGqQ7y9/AAAfZuGMnVoa7w9KvYSiG/+vI8Pfz9M4zA/lj7WTXs9nYPNZjD0ow38eSSDbg2C+OyBDphMl//ntPdENruOZ3LkVD5H0vKIT8vj6Kl8CszWvx3bt2ko7w5tRRUPt/Oe79Wf9jJ7TTxd61dn3phOl13PtWzy4t18uqFkJmHvJqHMuq+tvvdFRERERETj/1Iq53/n7gRZWVkABAYGArBlyxbMZjO9evWyH9O4cWPq1KljD882bNhA8+bN7cEZQN++fXn44YfZs2cPrVu3/tt1ioqKKCoqsn+dnZ1dXrckIiKVWEpOIesOpWGzXfg4L3dXejQKxsezQv1YrfTeXXGA7EILUTX8Gdq+tlNqMJlMTLixAbUDq/DUgp38sieZYbM38vH97Qj283RKTReTmlPE3HVHAHiid0OFB+fh4mLi9cEtuOW9Naw5mMa3WxIZ0u7yvs9m/nGY15ftO/f5TSUz3CKCfAjz9+T7bSf4ZU8yQ2dt5OMR7Qj1//tyjLlFFr7afAyAMdfXvfybusY9368JmQVmsgvMvHNXS33vi4iIiIiISKlVmFE+m83GxIkT6dq1K82aNQMgKSkJDw8PqlatetaxoaGhJCUl2Y/5a3B25vkzz53LlClT+Pe//13GdyAiIleT/Uk53DN7I6fyii/p+MggH6bf05qm4QHlXNm1YV9SNvM2lcwgmTygCa5OHgS/vVVNwqt6M/azGHYcy+SOD9cxd2R7GoT6ObyWYosNg/MvHPDBqkMUmK20rF1VS9ZdRL1gXx7v1ZA3ft7Hyz/u5YaGwYScI9Q6l1l/Cc46RgbSMNSPiCAfIoOqEFHdh1rVquDh9t/lMoe0q824z2LYdTyLgR+sY86I9jQJP/sTj9/8eYycIgt1g324oWFw2d3oNcLDzYX3hv39g3MiIiIiIiIil6vChGfjx49n9+7drF27ttyv9eyzz/LEE0/Yv87OzqZ2bed8ol1ERCqeA8n/Dc7qBJbMHLmQfSeziU/L444P1/Nivyju63RdqZZ/kxKGYfDSkr3YDLi1eRid6lZ3dkkAtI8IZNEjXRk1dzNHTuUzaMZ6Zt3Xli71g8rk/GarjUMpuaTkFJGaU0RKTuHp/xaRml1Eam4RKdmF5BX/fTnAc3mqTyN9H16Csd0i+WnXCXYfz2bS4j3MHN72oq+ZvTqOKaeDs8d7NeQfvS6+H1/7iEC+H9+VB6L/5HBqHnfOXM/7w1rT63TAabUZfLIuHijZ60yzpkREREREREScp0KEZxMmTODHH39k9erV1KpVy/54WFgYxcXFZGZmnjX7LDk5mbCwMPsxmzdvPut8ycnJ9ufOxdPTE0/PirnUkoiIONfBvwRnTcP9mTemI1WrXHj/zIy8Yp76dgcrY1N4cfEeNsSdYsqgFgR4uzuoaudZfziNhVuPc3urcLo1KJuZMsv3JrP+8Ck83Fx49paoMjlnWYkM8mHhI10Z91kMMUczuP+Tzbw+uAV3tq118RdfQEp2Ifd8vIlDKbllUmffpqF0rV8xQseKzs3VhTcHt+S26Wv5eU8SS3ed5NbmNc57/Mdr4nh1aSwA/7ipwSUFZ2dcV73k++eReVtYd+gUYz+P4YV+TXigawTL9ySRmFFAtSruDGp9Zd9PIiIiIiIiInJlTIZhnH/dn3JmGAaPPvooixYt4vfff6dBg7MHH7KysggODmb+/PkMHjwYgP3799O4cWP7nmfLli2jf//+nDx5kpCQEAA++ugjnnrqKVJSUi4pJNOGgSIiAnAoJZdhH20kLbeIJjX8+XLsxYOzMwzD4JN1R3h9WSxmq0HtQG+m392GlrWrlm/RTnIqt4hXl8aycOtx+2O3NAvj+X5R1KpWpdTnLbJY6f3OahLS85nQsz7/17dRWZRb5grNVp76didLdpwASkKUib0alGqmV3peMcM+2sCB5Fx8PFypHViFYD9PQvy8Tv/X0/7fEH8vAqt44OJy/vOZTCZ8PFw16+wy/Wf5fqb9doggXw9WPH4D1Xz+3vtz1sbz8o97AXjspgY80bthqa5lttqYtHgP8zcnAHBvxzrEnsxma0Jmhf6+FxERERERqYw0/i+l4dTw7JFHHuHLL79k8eLFNGr030GCgIAAvL29AXj44YdZunQp0dHR+Pv78+ijjwKwfv16AKxWK61atSI8PJw333yTpKQkhg8fzpgxY3jttdcuqQ41j4iIHErJ5e7ZG0nNKSKqhj9fjul4zsHzi9lxLJMJ87dyLL0Ad1cTz9zcmNHXR141QYZhGCyISeS1ZbFk5psxmeD6+kGsP3wKq83Ay92F8T3qM7Z7XbzcXS/7/DN+P8wbP+8j1N+T357sgY9nhZgkf042m8F/Vuzng1WHARjZJYJJ/Ztc1nJ7OYVm7pm9iV3Hswj19+Tbh7pQO7D04aOUXpHFSv/313IwJZdBbWryzl2tznp+7rp4/r2kJDh79Mb6PNG74RX1tWEYzFkbz6tLYznzr3F3VxPrnrnxkvddExERERERkYvT+L+UhlPDs/MNOMydO5eRI0cCUFhYyJNPPsn8+fMpKiqib9++fPjhh2ctyXj06FEefvhhfv/9d3x8fBgxYgSvv/46bm6XNuCm5hERubYdTi2ZcZaaU0TjMD++HNuJwFIEZ2dkFZh5duFOlu5KAuCmxiG8PaRlqcK4iuRQSg7PLdrN5vh0ABqH+TFlUHNa16lG7MlsJv+wx/7cddWrMHlAE25sHHrJ50/JLqTn27+TV2zl3aEtuaOSLF332YYjTFq8B4A729bi9UHNcXO9wNSw0wqKrYz4ZDObj6QT6OPBNw92on6IX3mXKxewNSGDwTPWYxgwd1R7ejYqWdXg0/VHmPxDyd/xhJ71ebLPlQVnf7VibzKPzd9GgdnK4Da1+M9dLcvkvCIiIiIiIlJC4/9SGk4NzyoKNY+IyLUr7nRwllJGwdkZhmHwxaYEXv5xL8UWGzUCvJh9fzua1Qwog6odq9BsZfpvh5i1+jBmq4G3uyuP927AqK6RuP8lJDIMgx92nOC1pbEkZxcBJcHhpAFNuK66z3nPb7HaSMstZsqyWBZvP0Gr2lVZ+HCXy5rB5WwLtyby1Lc7sdoMbmkWxtRhrfB0O//MuyKLlTGfxrDmYBp+Xm7MH9upUn5vXI1eWrKXT9bFEx7gxS+Pd2fRtuP2cPSRHvV4qm+jMp9Jui8pm6W7kniga8QlLxUrIiIiIiIil0bj/1IaCs9Q84iIXKvi0/IY9tEGkrOLaBTqx5djO1Ld9+J7ZV6OPSeymPDlNuLT8qhaxZ2vx3WmUVjlmV205mAqL3y/m6On8oGSMOzftze94L5muUUWpv12kDlr4rHYDDxcXRh1fQShfl6k5BSRmlNESk4hqad/n55fzF//NbLokS60rlOtvG+tzP28O4nH5m+j2Gqje8NgZt3XFm+PvwdoFquN8V9u5Zc9yVTxcOXz0R1oe12gEyqWc8kvtnDz1DUkpOfTolYAOxOzAHjohno8c3PZB2ciIiIiIiJSvjT+L6Wh8Aw1j4jItehIWh7DPtpIUnYhDUN9+XJsJ4LKODg7I6fQzPA5m9l+LJMgX08WPNSZyKDzz8SqCLYlZDDj98Ms35sMQJi/F/+6rQl9m4ZdcnhwKCWXfy/Zw5qDaRc91tXFRJCvB0Pb1+GJ3g2vqHZnWnMwlXGfbaHAbKV9RDXmjGyPv5e7/XmbzeDJBTtYtO04Hq4uzB3Vnq71g5xYsZzL+kNp3PPxJvvXD95Ql3/e3FjBmYiIiIiISCWk8X8pDYVnqHlERCqCX2OTeeH73fRrXoOnb26Mh9vF94y6XDmFZtYeTOPXfSms2JtMVoGZBiG+zB9XfsHZGVn5ZoZ+tIF9STnUrOrNNw91pmZV73K95uWy2Qx+P5DCzD/i7HuXuZjg/s4RPNmnIX5/CYEulWEY/LIniQUxiXh5uBLs60mIv+fp/3rZv65WxQPXSrRM44VsOZrOyLl/klNooVlNfz4d1YHqvp4YhsEL3+9m3qYEXF1MzLyvLb2bXPqecOJY//phD9Hrj/Bg97r88xYFZyIiIiIiIpWVxv+lNBSeoeYREXG2hFP59Ju2hpxCCwAtagUw7e7WF9wn61IdPZXHr7Ep/LYvhU3xpzBb//tjr1GoH1+M6UiwX/kGZ2ek5hQxdNYG4tLyiAzy4esHOxHi5+WQa19IscXGDztO8NHqwxxIzgXA3dXE7a1q8mD3ujQIrTzLTFYUe05kcf+czZzKK6Z+iC9fjO7I3HXxzFodh8kEU4e24vZWNZ1dplzEqdyiMl/KVURERERERBxL4/9SGgrPUPOIiDhTscXGkJnr2ZGYRaNQP5KyC8kqMOPn6cbrg1vQr0WNyzqfYRhsTchg+Z5kVsYmczg176zn6wb5cGPjEG6MCqF9RCDurmU/w+1CTmQWMGTmBo5nFtA4zI+vxnWiahUPh9ZwRk6hma82H2PO2niSsgsB8PV0456OdRjVNYIaARVrZlxlczg1l/s+3sTJrEL8vdzIPh0OTxnUnLs71HFydSIiIiIiIiLXBo3/S2koPEPNIyLlr9Bs5Zc9SWTmmy96bKvaVWlZu2r5F1VBvPzjXuasjSfA252l/+gGwGPzt7HlaAYA93Wqwwv9muDl7nrB8xSarfyw4wTR646w92S2/XE3FxMdIgNLArPGIdQN9i2/m7lER0/lMWTmBlJyimhZuyrzxnTE19Ot3K9rtRnsS8rmz/h0Nh9JZ83BNPtsv2A/Tx7oGsk9HesQ4H35yzPKuSVm5HPfx5s4ciofgBf6RTGmW10nVyUiIiIiIiJy7dD4v5SGwjPUPCJSvgrNVsZ8GsPaQ2mX/Jo7Wtfk2VsaE+Lv/CX9ytOKvcmM/SwGgNn3t7Pv/2S22nh3xQE+/P0wAI3D/Pjg3jbUO0fwlZRVyBcbj/Ll5gTS84oB8HJ34ZZmNegVFUq3hkH4l2KvrvJ2IDmHobM2kJFvpmNkINGjOuDtceGA8HIVW2zsOp7Jpvh0/oxPJ+Zohj0sO6NusA8Pdq/LwNY18XQr2+tLiZScQl5fuo8211Xjvk7XObscERERERERkWuKxv+lNBSeoeYRkfJTaLYy9rMY1hxMw8fDlR6NQsB0/uPziyz8fiAVwwAfD1cm9mrIyK4RDl9a0BESM/Lp9/5asgrMjL4+khf7N/nbMX8cSOWJr7dzKq+YKh6uvHpHM+5oXQvDMNh2LJO5646wbNdJLLaSH2XhAV7c3yWCYe1rO20pxMuxKzGLe2ZvJKfIQo9GwXw0vB0eblf+d/3jzhN8sfEo2xIyKbLYznrO19ONNtdVo0NENTpEVqfdddVwcbnAN6WIiIiIiIiISCWm8X8pDYVnqHlEpHz8NTir4uHKpw90oH1E4EVftysxixcX72b7sUwA6of48q8BTbm+QVA5V+w4ZquNobM2sDUhk5a1AljwUJfzhkbJ2YX846ttbIxLB6Bf8xokZuSzIzHLfkyHiEBGdY2gd5NQ3CpZ0PjnkXSGz9lEodnGrc3DeH9Y6yu6h4/XxPHKT7H2r6v7eNA+IpD2kYF0iAgkqoZfpfszEhEREREREREpLY3/S2koPEPNIyJlr9BsZdznW1h9IJUqHq5Ej+pAh8iLB2dn2GwG321N5PVl+zh1einCW5uH8Xy/JtSs6l1eZTvMlGWxzPojDj8vN5Y+1o3agVUueLzVZjDtt4O89+tBzvzU8nB14bZW4YzsEkGzmgEOqLr8rDmYyujoGIqtNga0DOetO1tcdI+3c5n+20HeXn4AgJFdIriv03XUC/bBZNLMMhERERERERG5Nmn8X0pD4RlqHhEpW4VmKw9+voU/DqTi7e7K3FHt6VS3eqnOlVVg5t0VB/hswxFsBni7uzLhxvqM6RZZafenWrUvhVHRfwIw49423NK8xiW/dv3hNGb+EUf766pxd8c6BPl6lleZDvfLniQembcVq82gec0AZtzXhlrVLhwqnmEYBm/9st++R9wTvRvy6I31FZqJiIiIiIiIyDVP4/9SGgrPUPOISNkpslh56PMtrNqfipe7C3NHdqBzvdIFZ38VezKbyYv3sPlIydKFzWr689W4zvh6ul3xuR3pZFYBt763hox8M/d3vo6Xbm/m7JIqlPWH0pgwfxvpecUE+ngw/e7WdKl/4eU6DcPg30v2Er3+CADP3xrF2O51HVCtiIiIiIiIiEjFp/F/KQ1teiIiUkaKLFYe/mKrPTj7ZET7MgnOAKJq+PP1g514b1grAn082H08m4lfbcdqqzyff7BYbfxj/nYy8s00DffnuVujnF1ShdOlfhA/TOhK85oBpOcVc9+cTXy0+jDn+5yL1Wbw3KJd9uDs5dubKjgTERERERERERG5QgrPRETKQJHFyvh5W/ltXwqebi7MGdH+ojOGLpfJZOL2VjWZM6IdHm4urIxN5s1f9pXpNcrT1JUH2XwkHV9PNz64p02p9vS6FtSqVoUFD3Xmzra1sBnw2tJ9PDp/G/nFlrOOs1htPPnNduZvPoaLCd66swXDO0c4p2gREREREREREZGriMIzEZErVGyxMX7eNlbG/jc461rGwdlfta5TjbfubAHArD/i+HZLYrldqyxk5BUzb9NRPvj9EACvDWpORJCPk6uq2LzcXXnrzha8fHtT3FxM/LjzJHd8sJ4jaXlAyffco/O38f32E7i5mHhvWGuGtKvt5KpFRERERERERESuDtrzDK15KpVLodnKFxuPcjg196LHVqviwcguEYT4ezmgsmuTzWbw6Pxt/LTrJB5uLswZ0Y5uDYIdcu3/LN/PtN8O4e5q4suxnWgfEeiQ615ModnKn0fSWXsojXWH0thzIpszP2nu7lCHKYOaO7fASibmSDoPz9tKak4R/l5uvHlnC77+8xir9qfi4erCB/e2oXeTUGeXKSIiIiIiIiJSIWn8X0pD4RlqHqk81h1K44XvdxN/evbJpfD1dGNirwaM6BKBu6smm5a1t3/Zz/RVJQHWxyPac0NDxwRnUBLcjf9yK8t2JxHo48Hi8V2pHVjFYdc/w2y1sedENutOh2UxRzMottjOOqZRqB+9m4Qy4cb6Wq6xFJKzC3lk3la2HM2wP+bl7sJHw9vR3YHfcyIiIiIiIiIilY3G/6U0FJ6h5pGKLy23iFd/imXRtuMAhPp7MrR9HTxcTed9jWHAyn0p7DiWCUCDEF/+fVvTMt+H61q2aFsij3+9AyjZb8oZy+blF1sYMnMDe05k0yjUj+8e6YKvp1u5XrPQbGVbQiab49P580g6WxMyyC+2nnVMjQAvutYP4vr6QXSpV12zH8tAscXGSz/u4YuNCfh4uPLJyPZ0rFvd2WWJiIiIiIiIiFRoGv+X0lB4hppHKi6bzeCbmGNMWbaPrAIzJhOM6BzBk30a4uflfkmv/3ZLIq//vI/0vGIA+rWowfO3RhFe1bu8y3cawzD440AqWQVmujcIppqPR5lfI+ZIOvfM3kSx1cZDN9Tjn7c0LvNrXKqTWQXcNn0dqTlF3NQ4hI/ub4ery/mD1cuVVWBmy9F0NsdnsDn+FLuOZ2G2nv2jI8DbnQ6RgXRrEETX+kHUDfLBZCq7GuS/Yo6kE+rv5ZRZhiIiIiIiIiIilY3G/6U0FJ6h5pGK6UByDs8t3EXM6WXamtTw57VBzWlVu+plnysr38w7K/bz+caj2Azwdndlwo31GdMtEk+3q2sJvYy8Yp75bifL9yYD4GKCNnWqcVNUKDdFhdAgxPeKQ51j6fkM/GAdp/KK6dMklJn3tcWlDMOq0th+LJOhszZQZLExrntdnrs1qkzOu3BrIv/8bhfF1rOXYQz196RDZHU6RFSjQ2R1GoT4Ov3PQERERERERERE5H9p/F9KQ+EZah6pWArNVqb9dpBZf8RhsRlU8XDlid4NGdklArcr3LNs74lsJv+wmz+PlARyEdWrMPm2pvRsFFIWpTvd+sNpPPH1DpKyC3F3NREZ5MOB5NyzjqlVzZubGodwY1QoneoGXnZ4mFNoZvCM9RxIzqVpuD8LHupMFY/yXSbxUv2w4wSPzd8GwJt3tuCuK1xGcmtCBsNmbaTYauO66lXoGBl4OjALpHagt2aWiYiIiIiIiIhIhafxfykNhWeoeaTi2BR3iqe+3UlCej4AvaJC+fftTalZhkssGobB4u0neHVpLKk5RQCMvj6SZ29pfMXhnLOYrTbeXXGAGX8cxjCgbrAP7w9rTbOaARzPLOC3fSn8GpvM+sOnKLb8dwZVFQ9XejcJ5aEb6hFV4+K9b7HaGPNZDL/vTyXEz5PFE7pSI6BiLX/5zvL9vP/bIdxdTcwb04kOkYGlOk9KdiH9p60lJaeIm5uGMeO+NgrLRERERERERESk0tH4v5SGwjPUPHJh8Wl5rDuURo0ALyKDfKgdWAX3Mg6ZiixW3ll+gI/WxGEYEObvxb9vb0rfpmFlep2/yik08+6Kg3yyLh6Abg2CmH53GwKqXHwvtYrkSFoe//hqGzsSswC4u0NtXuzf5JyzwfKLLaw7dIrf9iXza2wKKafDQ4BeUSGM71mf1nWqnfda/16yh7nrjuDp5sI3D3amZSmW0CxvNpvBhPlbWboriUAfD74a14mGoX6XdY4ii5W7P9rI1oRMGoT4smh8V3w9K8bsOhERERERERERkcuh8X8pDYVnqHnk3Kw2g4/XxPGfFQfOmq3k6mKiTmAVIoN87L/qBvlQP9SXED+vy75O7MlsHv96O/uScgAY2q42L/SPws/LMSHW0l0nefKbHRSYrURUr8Ls+9vR4DLDFmcwDIOFW48zafFu8oqtBHi78/qg5tzSvMYlv377sUw+XhvP0l0nOfN/wq71qzO+R30616t+1kyreZuO8vyi3QB8cE8b+rW4tOs4Q0GxlbtmbWDX8SyqVnHn01EdLivoe3bhLuZvTsDfy40fJlxPRJBP+RUrIiIiIiIiIiJSjjT+L6Wh8Aw1j/zd4dRcnlqwg60JmQC0rBWA2WoQn5ZHgdl63td1qhvIXe1qc0uzGnh7XHgvLXs4t/wAxVYb1X08eH1wC3o3CS3LW7kke09kM/azGI5nFuDr6cbUoa3o5YQ6LlV2oZkXFu3mhx0nAOgYGci7Q1sRXsrlLQ+n5jLj98N8v+04FlvJ/xJb16nKhJ71ubFxCOsOnWLE3M1YbQZP9m7Iozc1KLN7KS+Z+cWMmPsnO45l4uvpxscj2tGpbvWLvu7LTQk8t2gXJhPMHdmeHlfJfngiIiIiIiIiInJt0vi/lIbCM9Q88l9Wm8HcdfG89ct+iiw2/DzdeHFAE4a0rYXJZMIwDJKzi4hLyyU+LY/41LyS/6blEX8qzz57yc/TjQGtwrmrXW1a1gr4215Rx9LzefKbHWw+kg6U7G32+uDmBPl6OvqW7U7lFvHIvK1sik/HZIInezdkfM/6FW6fq1X7U3jx+90kZhTg6mLi8V4NeLhHfVxdrrzOxIx8Plodx1d/HrPPNoyq4c/xjHyyCy0MbBXOu0NbVbg/k/PJLbIw5tM/2RiXjqebCzPva0vPxucPw7YcTWfYRxsxWw2e6tuI8T3rO7BaERERERERERGRsqfxfykNhWeoeaREfFoeTy3YQczRDKBkD7A3Bre45NlMJzIL+G5LIt9sOcax9AL74w1DfbmrXW3uaF2TQB8Pvt2SyL+X7CW3yIKPhyuTBzRlSLtaFSKQMVttvLRkL59vPApAv+Y1eGtIi3PuH+Zo8Wl5vPzjXn7blwJA7UBv3hvWmjYX2KOstFJyCpmzJp4vNh4lr7hkpmHb66oxb0xHvNwvPKOwoik0Wxk/byu/7kvBzcXE1GGt6N8i/G/HJWcX0n/aWlJziri1eRgf3NOmQnxPioiIiIiIiIiIXAmN/0tpKDxDzXOts9kMotcf4c1f9lFotuHr6cYL/aIY2r52qcIDm81gY/wpFsQksnTXSYpOz2ByczHRINSP2JPZALSPqMZ/hrSiTvUqZXo/ZeHLTQlMWrwbi82gSQ1/Prq/LbWqOafO3CIL0347yCdr4zFbDdxdTYzqGsmjN9Yv933hMvOL+XT9UY6eyuO5flFOnRl4JcxWG098s4MlO07gYoIpg5oztH0d+/NFFitDZ21k+7FMGof58d3DXfDxdH5gKiIiIiIiIiIicqU0/i+lofAMNc+1LC41l38u3MXm+JLlE7vWr84bg1uUWVCUVWBmyY4TLIg5xo7ELADcXU080bsR47rXLZOlBsvL5vh0Hv5iC6fyiqnu48G0e1rTpV7QFZ0zI6+Y7EIzNat64+bqcsFjbTaDRduO8/rP+0jNKQKgR6NgJvVvQt1g3yuq41pktRm88P1u5m9OAOCFflGM6VYXwzD453e7+DrmGAHe7vwwoSvXVfdxcrUiIiIiIiIiIiJlQ+P/UhoKz1DzXGsOp+byy54kftmdZA+0qni48tytUdzbsU65LVW3LymbX2NTuLFxCFE1Ksf32fHMAsZ9FsOeE9m4mOCxmxrw6I0NLjv0s1htzFodx9SVBzBbDdxcTNQOrEJE9SpEBPkQGeRDRPWSXzWrebP7eBaTf9jD9mOZAEQG+fBi/yhubBxaDnd57TAMg9eX7WPW6jgA/nFTA4J8PXhx8R5cTDB3VAduaBjs5CpFRERERERERETKjsb/pTQUnqHmudoZhsGeE9n8vDuJX/YkcTAl1/6cyQQ3NAzm5dubUTuw4i2fWBEUFFuZtHg3C7YkAtC5bnXeG9aKEH+vS3r9weQcnlywg51/mXlntp7/fzvuriYsNgPDAB8PVx69qQGjukbg6Va59hqrqAzD4INVh3h7+YGzHv/nLY156IZ6TqpKRERERERERESkfGj8X0pD4RlqnqvVrsQsFm07zi97kjieWWB/3M3FROd61bm5WRi9m4QS4ndpIdC1buHWRF74fjf5xVaq+3jw7tBWdL/ALCWL1cbsNfG8u+IAxVYb/l5u/Ou2pgxsVZOk7EKOpOURfyqPI2l5HDmVz5G0PI6m51N8eo+4QW1q8s+bG19ySCeXJ3pdPP9asheA/i1qMO3u1uU261JERERERERERMRZNP4vpaHwDDXP1eqdFQd4/9eDAHi5u9CjYQh9m4VyY6NQAqq4O7m6yulwai7j521lX1IOAI/0qMcTvRv+bf+yQym5/N+CHfZlF29sHMKUQc0JvUgQZrUZnMwqwMVkIryqd7ncg/zXyr3J7EjM5OEe9aji4ebsckRERERERERERMqcxv+lNBSeoea5Wu1LyuajP+Lo0zSMGxoG4+2hZf/KQqHZyss/7mXepgQA2l1Xjffvbk14VW+sNoNP1sbz1vL9FFts+Hm6MWlAE+5sW0uzmkRERERERERERMThNP4vpeFy8UPKz+rVqxkwYADh4eGYTCa+//77s543DINJkyZRo0YNvL296dWrFwcPHjzrmPT0dO699178/f2pWrUqo0ePJjc3F5HGYf68M7QVNzcLU3BWhrzcXXn1juZMv6c1fp5uxBzN4Nb31zB/cwJ3zdrAq0tjKbbYuKFhMMuf6M6QdrUVnImIiIiIiIiIiIhIpeHU8CwvL4+WLVvywQcfnPP5N998k/fff5+ZM2eyadMmfHx86Nu3L4WFhfZj7r33Xvbs2cOKFSv48ccfWb16NePGjXPULYhcs/q3COfHx66nec0AMvPNPLtwF1uOZuDr6cYbg5sTPao9NQK09KKIiIiIiIiIiIiIVC4VZtlGk8nEokWLGDhwIFAy6yw8PJwnn3yS//u//wMgKyuL0NBQoqOjGTZsGLGxsTRp0oQ///yTdu3aAfDzzz9z6623kpiYSHh4+CVdW9M2RUqvyGLl9WX7iF5/hOvrB/H64BbU1H5lIiIiIiIiIiIiUgFo/F9Kw6kzzy4kPj6epKQkevXqZX8sICCAjh07smHDBgA2bNhA1apV7cEZQK9evXBxcWHTpk3nPXdRURHZ2dln/RKR0vF0c2XygKbs/ldfPh/dUcGZiIiIiIiIiIiIiFRqFTY8S0pKAiA0NPSsx0NDQ+3PJSUlERISctbzbm5uBAYG2o85lylTphAQEGD/Vbt27TKuXuTa4+Pp5uwSRERERERERERERESuWIUNz8rTs88+S1ZWlv3XsWPHnF2SiIiIiIiIiIiIiIiIVAAVNjwLCwsDIDk5+azHk5OT7c+FhYWRkpJy1vMWi4X09HT7Mefi6emJv7//Wb9EREREREREREREREREKmx4FhkZSVhYGL/++qv9sezsbDZt2kTnzp0B6Ny5M5mZmWzZssV+zG+//YbNZqNjx44Or1lEREREREREREREREQqN6duUpSbm8uhQ4fsX8fHx7N9+3YCAwOpU6cOEydO5JVXXqFBgwZERkby4osvEh4ezsCBAwGIiori5ptvZuzYscycOROz2cyECRMYNmwY4eHhTrorERERERERERERERERqaycGp7FxMTQs2dP+9dPPPEEACNGjCA6Opqnn36avLw8xo0bR2ZmJtdffz0///wzXl5e9tfMmzePCRMmcNNNN+Hi4sLgwYN5//33HX4vIiIiIiIiIiIiIiIiUvmZDMMwnF2Es2VnZxMQEEBWVpb2PxMRERERERERERERuUpo/F9Ko8LueSYiIiIiIiIiIiIiIiLiaArPRERERERERERERERERE5z6p5nFcWZlSuzs7OdXImIiIiIiIiIiIiIiJSVM+P+2sFKLofCM+DUqVMA1K5d28mViIiIiIiIiIiIiIhIWTt16hQBAQHOLkMqCYVnQGBgIAAJCQlqHpFylJ2dTe3atTl27Jg25xQpR+o1EcdRv4k4hnpNxHHUbyKOoV4TcZysrCzq1KljzwFELoXCM8DFpWTrt4CAAP2wEnEAf39/9ZqIA6jXRBxH/SbiGOo1EcdRv4k4hnpNxHHO5AAil0LfLSIiIiIiIiIiIiIiIiKnKTwTEREREREREREREREROU3hGeDp6cnkyZPx9PR0dikiVzX1mohjqNdEHEf9JuIY6jURx1G/iTiGek3EcdRvUhomwzAMZxchIiIiIiIiIiIiIiIiUhFo5pmIiIiIiIiIiIiIiIjIaQrPRERERERERERERERERE5TeCYiIiIiIiIiIiIiIiJymsIzERERERERERERERERkdMUnomIiIiIiIiIiIiIiIicpvBMREREROQ8LBYLAIZhOLkSEREREREREXEUN2cXUN4Mw8BkMv3t9yJSdoqLi5kzZw7Vq1enXbt21K1b19kliVyVzGYzixYtIjg4mCZNmhAaGurskkSuSoZhYBgG48aNIzMzk2+//Vb/hhQpZ0lJSbi5uREUFKT3bSLlSGMkIs6hfhMRqXxMxlX6Mdri4mJeeeUVCgsLqV27No8++qizSxK5Ki1cuJAHHniAhg0bcuLECXx9fXnmmWcYNWqUs0sTuarMmDGD559/nqioKGJjY2ndujXPPvssvXr10hsxkXJw7NgxoqKiyM/PZ/HixQwYMACbzYaLixZuEClLZrOZ8ePHs27dOiZOnMjYsWOdXZLIVUljJCKOU1xczPvvv4+/vz+tWrWiQ4cOzi5J5KpkNpv54osvqFq1KlFRUTRu3NjZJclV5qp89//NN99Qq1Yt1qxZQ2JiIhMnTuSZZ54BtOSOSFkyDIOPPvqIcePGsXnzZpYvX86wYcN48MEH+eOPP5xdnshVoaioiFdffZUPP/yQDz/8kN9//53vvvuO4OBgZs2apeBMpJxs3ryZXr168dhjjzF+/HgABWciZWz//v20bduWPXv2MGPGDPr06WOf+SkiZUdjJCKOs3TpUmrUqMG3337L+++/z4ABA5gyZYqzyxK56syaNYvQ0FA++eQTJk6cyKBBg/jmm28AsNlsTq5OrhZX3QhARkYGM2bM4PHHH2fVqlXMmzePqVOnMm/ePA0wipSBv7652rVrF+vXr2fw4MEANGnShH/961/06tWLp59+muPHjzurTJFK70yvZWVlkZGRwbhx4xg2bBju7u707NmTyMhIcnJyMJvNTq5UpPI702+GYdjfaHl6euLq6srDDz9Mbm4ur7zyijNLFLkqLVmyhIiICNatW0f37t3x9vbGZDLpPZtIGdIYiYhjzZ07l6FDh7Jx40ZWrFjBlClTeP7554mOjqa4uNjZ5YlUehaLhalTp/LBBx8wffp01qxZw5IlS+jVqxdvvvmmVguRMnXVfSdt27aNtWvXMmTIEABMJhMZGRmMGTPmrHW9ReTyTZo0iejoaPvXtWrVwmQyceLECQD7PwRnzpzJli1b+Pnnn51Rpkil99deCw4OZvjw4faldc4M7IeFhZGfn4+Hh4ezyhS5Kvy130wmk/2N1oYNG6hVqxaNGjXi//7v/3j99ddJS0tjwYIFnDx50okVi1ReZ96HWa1WCgoKWLduHX369CEtLY277rqLvn370q1bN1544QUsFouTqxWpvP465qExEpHyZbVa7b+Pi4tj48aN3HDDDQDUqFGDBx54gBEjRjBt2jS2bdvmrDJFKr0zvVZUVERubi533nknw4YNA6BFixY0bdoUV1dXUlNTnVmmXGUqfXj22WefcejQIfvXzZs3x8/Pj3feeYeYmBiefvppXnrpJRYtWkSTJk34/vvvKSwsdGLFIpXPzJkz8fPzY8mSJXTt2tX+uNVqpW/fvnzxxRcAeHh4YLFYqFOnDqNHj2batGnOKlmkUjpXr5lMJlq2bAlw1ieoli1bZl87XwOMIpfvfD/bzgTUJpOJOnXqADBx4kSqVatGSEgI0dHR6jmRUvhrUO3q6oq3tzd79+4lIyODd999F1dXVyZPnkyfPn148803ee2118jOznZu0SKV0P9+4LFFixYaIxEpJy+88ALPP/+8/evIyEiKi4vJyMgAoKCgAIC33nqLkydPsnTpUs0+EymFv/aaj48P9957L5MmTcLFxcX+AZCqVauSl5dHSEiIM0uVq0ylDc9sNhuDBg1i5MiRfP/99/YfSMHBwcyePZtTp04xfPhwfvjhB5YtW8ZHH31Enz59eOihh4iJiXFy9SKVw6FDh+jYsSPPPPMMs2fPZtu2bTRs2ND+fHBwMB07duTo0aN89dVXZ7126NChnDx5kgMHDji6bJFK52K9BiWfCD7zD8OsrCzi4uLo2bMnAG5ubvbjtLa3yIVdrN/OBNSHDx8mIiKC/fv30759e/sg/rPPPkvt2rX1KX2RS3SuoPrMz6rbb7+dt99+m8WLFzN58mQGDhzIiy++yKuvvsonn3xCQkKCM0sXqVTO96GQoKAgjZGIlLHFixcTFhbG8uXLCQ8PJz09HfjvWOXMmTMB8Pb2xmw2ExQUxNixY4mOjsbV1dWZpYtUKv/ba6dOnQJKgmoo6bkzs6h//vlnWrdujclk0vYWUmYqZXh25pP3NWvWpF27drzzzjvs27fP/vzgwYOZMWMGwcHBTJ06ld69e9OxY0emTp2K1Wplx44dTqxepPL4888/OXToEC+//DLDhg0jMzOTr7/+mo0bNxIXFweUDHpERkYyc+ZMTp06ZR/EP3jwIL6+vgQEBDjzFkQqhQv12tGjR8861mQycfLkSaxWK23btgVg586dPP300wBa21vkIi6l33JzczGbzTzyyCM0b96cnj17snnzZm677TaeeOIJAO0RI3IRFwqqz/ys6ty5MyEhIXh7e9OwYUN7qPbUU0+RlpamD2GJXIJL+RCWxkhEyk5eXh5z5sxh/PjxbN68mccee4zAwECgZGZ1r169KCoq4r333gP++4GRkSNHkpaWpqUbRS7RuXqtevXqZx3j4uKC1WrFYrGwY8cOunXrBoC7u7v9GH3AWK5EpRxhc3FxIS0tjQ0bNrBy5Uq8vb2ZPn26/ZMeAGlpaezdu5fOnTsDJcvLJSYmUq1aNe0PI3KJ7r77bm6++WZWrFjBgw8+SIsWLZg6dSoDBgygd+/exMTEUL9+fcaMGUNOTg533303GzduJCEhgRUrVtCpUyeCgoKcfRsiFd6Feu3GG29ky5YtZw3UL1u2jKioKPz8/Bg9ejRt2rQhJSUFwzA0G0bkIi7WbzExMfj6+hIVFUWvXr3YuHEj06dPp1GjRjz++OPExMRo0EPkElwoqI6PjwegS5cu9O3bl23btrFlyxZ7qLZ//35q1KiBr6+vM29BpFK41A9hpaamaoxEpAz8/vvvbNy4keeff56MjAz++c9/8sYbbzBv3jwAevXqRe/evXnnnXc4efIknp6eQMkHHoOCgvSzTeQSna/X5s+fbx/3MAwDV1dX0tLSSE1Nte83uH37dkaOHAnoA8ZyZSrld4/VasXX1xcfHx98fX2ZMmUKn332mf1NWHZ2Nl5eXoSFhfHkk09y7NgxkpKSmDx5Mr6+vvTu3dvJdyBS8SxYsICxY8fy3nvvsWvXLvvjY8eOJTY2ll27dvHee+/x7bffsnLlSpo2bcr9999PcnIyffr0Ye7cuZw8eZJRo0bRrl07kpOTef3117Ukgcj/KE2vjRgxgsTERKBkf7OlS5eyefNmwsPD2bVrF7t37yY6OhqTyaTZMCJ/UZp+u++++zh16hSTJ0/ms88+o02bNvbXde3albS0NFq3bu2M2xGpVC4UVPfq1YuYmBhCQ0OZMGECnTp1YuTIkcyZM4cDBw7w5ptvUqNGDdq3b+/s2xCp8C7lQ1hQ8sn7GjVqaIxEpJTODNYnJyfTtWtXVq1aRdu2bdm+fTvr169nxIgRPProo9hsNp544glq1apF//79+frrrzl8+DDz58+nadOmREREOPdGRCq4i/Xa8OHD+cc//sGJEyfs4x8rV66kbt26hIeHM3r0aDp06EBmZiY2m00fMJYrY1Rg33zzjTFmzBhj6tSpxs6dO8967tChQ0ZERISRkZFhGIZh9OzZ02jSpIkRHh5uvPPOO4bFYjHmzZtnVKlSxWjevLlRo0YNo2fPnsbhw4edcCciFVdaWppx5513GmFhYcZDDz1kXH/99UbNmjWNuXPn2o+ZMWOGsXLlyrNel56ebnh4eBhff/21/bGsrCzj4MGDRkxMjKPKF6k0rrTXvvnmG8MwDCMzM9Po3r27Ua9ePWPJkiWOvAWRSqOs+s1qtTqybJFK63zv21atWmXUq1fP6Ny5s7Fw4UIjMTHR2L59uzFgwAAjKirKOH78uGEYhpGSkmLcfvvtRrNmzYzrrrvO6N69uxEXF+es2xGpsErTa02aNDGSkpKMoqIijZGIXIbz9dv8+fONgIAA45FHHjEmTZpkFBcXG4ZhGNHR0UbHjh2Nt99+2zAMw0hKSjJuvvlm+1hlly5djPj4eGfcikiFVppe69Spk/HGG28YhmEYNpvNGDp0qOHq6mr4+fkZ7dq1M2JjY51yL3L1qZDh2fkGPKKjo+3H/Prrr8a9995rGIZhHDx40GjevLlhMpmMQYMGGenp6fbj9u7da6xYscJYt26dw+9DpDJYsGCB0aFDByMxMdH+2ODBg4169eoZ3377rWEYhpGfn/+312VlZRkRERHGiy++aH/MZrOVf8EildSV9toLL7xgf0wBtciFleXPNhE5v7IKqg3DMIqKioy0tDRjz549jipfpNK40l776quv7I9pjETkwi7WbzabzWjWrJlhMpnO6kGbzWYMHjzYGD16tFFUVGQYhmEUFhYaJ0+e/NuEABG58l4bM2aMYTabDZvNZgwbNsyIiIgwfvrpJ+fcjFy13Jw98+1cVq1aRUJCAjExMdSsWROAO++8k5dffhlfX18GDx5Mfn4+MTEx3H///Xz11Vc8+OCDNGnShF27dpGamkq1atUAiIqKIioqypm3I1Khffnll9SqVYuaNWuSm5uLr68vt912GwsXLuTDDz+ke/fuBAcHYxjGWcvBbdiwAW9vb+666y77Y1ouTuT8rrTXhg4dan+sbdu2zrgFkUqjLH+2icj5ne992yuvvIKfnx+DBw9mxIgReHt7n/U6V1dX+9LDQ4YMAcDDw4Pq1av/bSN4EbnyXtu9e7f935IaIxG5sAuNSVatWpWBAwfyyCOPMH78ePLz87FYLLi5uWEymfDz82Pv3r32fQQ9PDwICwsjLCzMmbckUiFdaa/t3r0bN7eSaOOll16iQYMGzrwduUpVyD3P/nfAA+C2224jLi6ODz/8kIyMDHx8fMjJyeHkyZP89ttvTJs2jc8++4zY2Fg++eQTzGazk+9CpOJZvXo1v/zyCxaLxf5YgwYN2LNnD4B949rY2FhuvPFGCgsL+f7774GSYOzkyZMcOnSIWbNmMW7cOHr37k29evW0frDI/1CviTiO+k3EeS72vi01NRVvb++/9ZOCapHLc6W99tcPYYnIhZ2v3+Lj45k2bRqnTp3iwQcfpHfv3kybNo3ffvsNgKSkJE6cOMEDDzxgP5c+YCxyflfaa6NHj7afS8GZlBenh2eXO+CRn5/PTz/9RJcuXVi8eDE//PAD119/PRaLBQ8PDxYsWMDIkSNxd3d3yv2IVERpaWmMGDGCHj168Mwzz5CYmGh/7sEHHyQ9PZ0bbriBt956iy5duvD111/z3HPPkZeXx7FjxwAoLCxkxYoV9OvXjzfeeIPXXnuN9957D29vb/2DUOQ09ZqI46jfRBxLQbWIY6jXRBzncvutoKCAhQsX4uLiwrx58wgJCeGee+7h1ltvpVWrVpjNZvr16+eUexGpyNRrUmk5a73I1NRU4/777zdMJpPRsmXLszbNPHz4sBEcHGx0797dePPNN43OnTsbkZGRxq+//mq0bNnyrH1fztBeSyLnZjabjQ8//NDo27ev8fXXXxtVqlQxpkyZYhQWFtqPWbt2rTFmzBijTZs2xoQJE4zU1FTDMAxj+PDhxuDBg+3HpaSkGIsXL3b4PYhUBuo1EcdRv4k4zpW8bzuzf2BBQYHx6aefGg0bNjQiIyONL774wkl3I1JxqddEHOdK+81isRiGYRjJycnG8uXLjbfeestYtGiRc25GpAJTr0ll55Q9zywWCwsWLCA5OZmvvvqKUaNG8dVXX/H444/j6elJ3bp1WbRoEdHR0Xz11Vd06dKFyZMnExQURIsWLYiNjf3bOfXpYJFzc3Nzo02bNtSqVYsBAwawb98+3nnnHW6++WZatWoFQNeuXenatSvFxcX2tblTUlLYtm0bw4YNA8BmsxEcHMxtt93mrFsRqdDUayKOo34TcYwrfd+2d+9eALy8vLjllluoWrWq+k3kHNRrIo5TFv3m6uoKQEhICL1796Z3795OviuRike9JlcDk2E4Z+7+pk2bSElJYcCAAbz00ktMnz6d5cuX2wc8zvjfAY+bbrqJYcOG8fzzz2Oz2XBxcfrKkyIVnmEYZwXMNWvWpH///rz99tv4+fmd9XxhYSGurq58/PHHzJ49m08//ZTmzZs7q3SRSkW9JuI46jcRx9D7NhHHUK+JOI76TcQx1GtS2TntO69Dhw4MGDAAgEmTJuHu7s6MGTPIyckBsK/H7eHhQWFhIWazme+++w53d3f7J6jUOCKX5szgYXFxMQBTp07lk08+YePGjWc9f/z4cebOnUuXLl144YUXeOqppzS4KHIZ1GsijqN+E3EMvW8TcQz1mojjqN9EHEO9JpWd02aenXEmWV6wYAH33HMPS5cuPWsK5vHjx/nhhx/45JNPiIuLY/r06dx9991OrFjk6tClSxd8fHzsG2+mpqYSHBzM/PnzOXHiBE8++aSzSxS5KqjXRBxH/SZSfvS+TcQx1GsijqN+E3EM9ZpUVk4Pz/5KAx4i5c9iseDm5saePXto2bIl77zzDocPH2bt2rV8+umnNGvWzNklilwV1GsijqN+E3EsvW8TcQz1mojjqN9EHEO9JpVJhQjPNOAh4hwdOnQgJiaGOnXqMGvWLPr27evskkSuSuo1EcdRv4mUH71vE3EM9ZqI46jfRBxDvSaVUYUIz/5KAx4i5e/w4cMMHDiQuLg43n//fUaPHu3skkSuSuo1EcdRv4k4lt63iTiGek3EcdRvIo6hXpPKosLsuHf48GGaN2/Onj17mD17NkeOHFHjiJQTV1dXBg8eTFpamgYXRcqRek3EcdRvIo6h920ijqFeE3Ec9ZuIY6jXpLKpMDPPjhw5QnR0NM888wze3t7OLkdERERERET+h963iTiGek3EcdRvIo6hXpPKpsKEZyIiIiIiIiIiIiIiIiLOVmGWbRQRERERERERERERERFxNoVnIiIiIiIiIiIiIiIiIqcpPBMRERERERERERERERE5TeGZiIiIiIiIiIiIiIiIyGkKz0REREREREREREREREROU3gmIiIiIiIiIiIiIiIicprCMxEREREREREREREREZHTFJ6JiIiIiEiFM3LkSAYOHOjw60ZHR2MymTCZTEycOPGCx0ZERDB16tRLOm+PHj3s592+ffsV1ykiIiIiIiLlx83ZBYiIiIiIyLXFZDJd8PnJkyfz3nvvYRiGgyo6m7+/P/v378fHx6fMzrlw4UIOHz5Mhw4dyuycIiIiIiIiUj4UnomIiIiIiEOdPHnS/vuvv/6aSZMmsX//fvtjvr6++Pr6OqM0oCTcCwsLK9NzBgYGkp2dXabnFBERERERkfKhZRtFRERERMShwsLC7L8CAgLsYdWZX76+vn9btrFHjx48+uijTJw4kWrVqhEaGsrs2bPJy8tj1KhR+Pn5Ub9+fZYtW3bWtXbv3s0tt9yCr68voaGhDB8+nLS0tMuuOSUlhQEDBuDt7U1kZCTz5s0763nDMPjXv/5FnTp18PT0JDw8nMcee6xUfz4iIiIiIiLiXArPRERERESkUvj0008JCgpi8+bNPProozz88MMMGTKELl26sHXrVvr06cPw4cPJz88HIDMzkxtvvJHWrVsTExPDzz//THJyMnfddddlX3vkyJEcO3aMVatW8e233/Lhhx+SkpJif/67777j3XffZdasWRw8eJDvv/+e5s2bl9m9i4iIiIiIiONo2UYREREREakUWrZsyQsvvADAs88+y+uvv05QUBBjx44FYNKkScyYMYOdO3fSqVMnpk+fTuvWrXnttdfs5/jkk0+oXbs2Bw4coGHDhpd03QMHDrBs2TI2b95M+/btAZgzZw5RUVH2YxISEggLC6NXr164u7tTp04d7W8mIiIiIiJSSWnmmYiIiIiIVAotWrSw/97V1ZXq1aufNbsrNDQUwD4jbMeOHaxatcq+h5qvry+NGzcG4PDhw5d83djYWNzc3Gjbtq39scaNG1O1alX710OGDKGgoIC6desyduxYFi1ahMViKdV9ioiIiIiIiHNp5pmIiIiIiFQK7u7uZ31tMpnOesxkMgFgs9kAyM3NZcCAAbzxxht/O1eNGjXKtLbatWuzf/9+Vq5cyYoVK3jkkUd46623+OOPP/5Wt4iIiIiIiFRsCs9EREREROSq1KZNG7777jsiIiJwcyv9W5/GjRtjsVjYsmWLfdnG/fv3k5mZedZx3t7eDBgwgAEDBjB+/HgaN27Mrl27aNOmzZXchoiIiIiIiDiYlm0UEREREZGr0vjx40lPT+fuu+/mzz//5PDhw/zyyy+MGjUKq9V6yedp1KgRN998Mw8++CCbNm1iy5YtjBkzBm9vb/sx0dHRzJkzh927dxMXF8cXX3yBt7c31113XXncmoiIiIiIiJQjhWciIiIiInJVCg8PZ926dVitVvr06UPz5s2ZOHEiVatWxcXl8t4KzZ07l/DwcG644QYGDRrEuHHjCAkJsT9ftWpVZs+eTdeuXWnRogUrV65kyZIlVK9evaxvS0RERERERMqZyTAMw9lFiIiIiIiIVATR0dFMnDjxb0syloUjR44QGRnJtm3baNWqVZmfX0RERERERMqGZp6JiIiIiIj8RVZWFr6+vjzzzDNlds5bbrmFpk2bltn5REREREREpPxo5pmIiIiIiMhpOTk5JCcnAyVLMQYFBZXJeY8fP05BQQEAderUwcPDo0zOKyIiIiIiImVP4ZmIiIiIiIiIiIiIiIjIaVq2UUREREREREREREREROQ0hWciIiIiIiIiIiIiIiIipyk8ExERERERERERERERETlN4ZmIiIiIiIiIiIiIiIjIaQrPRERERERERERERERERE5TeCYiIiIiIiIiIiIiIiJymsIzERERERERERERERERkdMUnomIiIiIiIiIiIiIiIic9v/FbbDmudtkKAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1600x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split data and declare panel dataset\n",
    "Y_df = AirPassengersDF\n",
    "Y_train_df = Y_df[Y_df.ds<='1959-12-31'] # 132 train\n",
    "Y_test_df = Y_df[Y_df.ds>'1959-12-31'] # 12 test\n",
    "\n",
    "# Fit and predict with NBEATS and NHITS models\n",
    "horizon = len(Y_test_df)\n",
    "models = [NBEATS(input_size=2 * horizon, h=horizon, max_steps=100, enable_progress_bar=False),\n",
    "          NHITS(input_size=2 * horizon, h=horizon, max_steps=100, enable_progress_bar=False)]\n",
    "nf = NeuralForecast(models=models, freq='ME')\n",
    "nf.fit(df=Y_train_df)\n",
    "Y_hat_df = nf.predict()\n",
    "\n",
    "# Plot predictions\n",
    "plot_series(Y_train_df, Y_hat_df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7cb3919a",
   "metadata": {},
   "source": [
    "## 🙏 How to Cite"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2c669ec7",
   "metadata": {},
   "source": [
    "If you enjoy or benefit from using these Python implementations, a citation to the repository will be greatly appreciated."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0fed17d7",
   "metadata": {},
   "source": [
    "```\n",
    "@misc{olivares2022library_neuralforecast,\n",
    "    author={Kin G. Olivares and\n",
    "            Cristian Challú and\n",
    "            Federico Garza and\n",
    "            Max Mergenthaler Canseco and\n",
    "            Artur Dubrawski},\n",
    "    title = {{NeuralForecast}: User friendly state-of-the-art neural forecasting models.},\n",
    "    year={2022},\n",
    "    howpublished={{PyCon} Salt Lake City, Utah, US 2022},\n",
    "    url={https://github.com/Nixtla/neuralforecast}\n",
    "}\n",
    "```"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
